Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

PubMed

Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

2015-01-01

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.

Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-01-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the loadmore » and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.« less

A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

PubMed

Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias

2013-10-01

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

A Gaussian Processes Technique for Short-term Load Forecasting with Considerations of Uncertainty

NASA Astrophysics Data System (ADS)

Ohmi, Masataro; Mori, Hiroyuki

In this paper, an efficient method is proposed to deal with short-term load forecasting with the Gaussian Processes. Short-term load forecasting plays a key role to smooth power system operation such as economic load dispatching, unit commitment, etc. Recently, the deregulated and competitive power market increases the degree of uncertainty. As a result, it is more important to obtain better prediction results to save the cost. One of the most important aspects is that power system operator needs the upper and lower bounds of the predicted load to deal with the uncertainty while they require more accurate predicted values. The proposed method is based on the Bayes model in which output is expressed in a distribution rather than a point. To realize the model efficiently, this paper proposes the Gaussian Processes that consists of the Bayes linear model and kernel machine to obtain the distribution of the predicted value. The proposed method is successively applied to real data of daily maximum load forecasting.

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2014 CFR

2014-01-01

... computer software applications. RUS will evaluate borrower load forecasts for readability, understanding..., distribution costs, other systems costs, average revenue per kWh, and inflation. Also, a borrower's engineering...

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Residential Saudi load forecasting using analytical model and Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Al-Harbi, Ahmad Abdulaziz

In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.

Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steckler, N.; Florita, A.; Zhang, J.

2013-11-01

As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecastsmore » relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.« less

Forecasting of hourly load by pattern recognition in a small area power system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dehdashti-Shahrokh, A.

1982-01-01

An intuitive, logical, simple and efficient method of forecasting hourly load in a small area power system is presented. A pattern recognition approach is used in developing the forecasting model. Pattern recognition techniques are powerful tools in the field of artificial intelligence (cybernetics) and simulate the way the human brain operates to make decisions. Pattern recognition is generally used in analysis of processes where the total physical nature behind the process variation is unkown but specific kinds of measurements explain their behavior. In this research basic multivariate analyses, in conjunction with pattern recognition techniques, are used to develop a linearmore » deterministic model to forecast hourly load. This method assumes that load patterns in the same geographical area are direct results of climatological changes (weather sensitive load), and have occurred in the past as a result of similar climatic conditions. The algorithm described in here searches for the best possible pattern from a seasonal library of load and weather data in forecasting hourly load. To accommodate the unpredictability of weather and the resulting load, the basic twenty-four load pattern was divided into eight three-hour intervals. This division was made to make the model adaptive to sudden climatic changes. The proposed method offers flexible lead times of one to twenty-four hours. The results of actual data testing had indicated that this proposed method is computationally efficient, highly adaptive, with acceptable data storage size and accuracy that is comparable to many other existing methods.« less

Short term load forecasting using a self-supervised adaptive neural network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, H.; Pimmel, R.L.

The authors developed a self-supervised adaptive neural network to perform short term load forecasts (STLF) for a large power system covering a wide service area with several heavy load centers. They used the self-supervised network to extract correlational features from temperature and load data. In using data from the calendar year 1993 as a test case, they found a 0.90 percent error for hour-ahead forecasting and 1.92 percent error for day-ahead forecasting. These levels of error compare favorably with those obtained by other techniques. The algorithm ran in a couple of minutes on a PC containing an Intel Pentium --more » 120 MHz CPU. Since the algorithm included searching the historical database, training the network, and actually performing the forecasts, this approach provides a real-time, portable, and adaptable STLF.« less

Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yannan; Hou, Zhangshuan; Meng, Da

2016-07-17

In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

Short-term load forecasting using neural network for future smart grid application

NASA Astrophysics Data System (ADS)

Zennamo, Joseph Anthony, III

Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.

Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon

NASA Astrophysics Data System (ADS)

Yildiz, Baran; Bilbao, Jose I.; Dore, Jonathon; Sproul, Alistair B.

2018-05-01

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance.

NASA Products to Enhance Energy Utility Load Forecasting

NASA Technical Reports Server (NTRS)

Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.

2012-01-01

Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.

Load Forecasting of Central Urban Area Power Grid Based on Saturated Load Density Index

NASA Astrophysics Data System (ADS)

Huping, Yang; Chengyi, Tang; Meng, Yu

2018-03-01

In the current society, coordination between urban power grid development and city development has become more and more prominent. Electricity saturated load forecasting plays an important role in the planning and development of power grids. Electricity saturated load forecasting is a new concept put forward by China in recent years in the field of grid planning. Urban saturation load forecast is different from the traditional load forecasting method for specific years, the time span of it often relatively large, and involves a wide range of aspects. This study takes a county in eastern Jiangxi as an example, this paper chooses a variety of load forecasting methods to carry on the recent load forecasting calculation to central urban area. At the same time, this paper uses load density index method to predict the Longterm load forecasting of electric saturation load of central urban area lasted until 2030. And further study shows the general distribution of the urban saturation load in space.

Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coimbra, Carlos F. M.

2016-02-25

In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior inmore » real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.« less

Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

NASA Astrophysics Data System (ADS)

Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup

2018-04-01

Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.

Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

NASA Astrophysics Data System (ADS)

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

2010-02-01

This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2014-04-14

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation.more » We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.« less

A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting

PubMed Central

Wang, Hongguang

2018-01-01

Annual power load forecasting is not only the premise of formulating reasonable macro power planning, but also an important guarantee for the safety and economic operation of power system. In view of the characteristics of annual power load forecasting, the grey model of GM (1,1) are widely applied. Introducing buffer operator into GM (1,1) to pre-process the historical annual power load data is an approach to improve the forecasting accuracy. To solve the problem of nonadjustable action intensity of traditional weakening buffer operator, variable-weight weakening buffer operator (VWWBO) and background value optimization (BVO) are used to dynamically pre-process the historical annual power load data and a VWWBO-BVO-based GM (1,1) is proposed. To find the optimal value of variable-weight buffer coefficient and background value weight generating coefficient of the proposed model, grey relational analysis (GRA) and improved gravitational search algorithm (IGSA) are integrated and a GRA-IGSA integration algorithm is constructed aiming to maximize the grey relativity between simulating value sequence and actual value sequence. By the adjustable action intensity of buffer operator, the proposed model optimized by GRA-IGSA integration algorithm can obtain a better forecasting accuracy which is demonstrated by the case studies and can provide an optimized solution for annual power load forecasting. PMID:29768450

A stochastic post-processing method for solar irradiance forecasts derived from NWPs models

NASA Astrophysics Data System (ADS)

Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.

2010-09-01

Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2012 CFR

2012-01-01

... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by distribution...

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2013 CFR

2013-01-01

... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by distribution...

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2014 CFR

2014-01-01

... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by distribution...

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2011 CFR

2011-01-01

... power supply borrowers and by distribution borrowers required to maintain an approved load forecast on... forecasts by power supply borrowers and by distribution borrowers required to maintain an approved load forecast on an ongoing basis. All load forecasts submitted by power supply borrowers and by distribution...

A Load-Based Temperature Prediction Model for Anomaly Detection

NASA Astrophysics Data System (ADS)

Sobhani, Masoud

Electric load forecasting, as a basic requirement for the decision-making in power utilities, has been improved in various aspects in the past decades. Many factors may affect the accuracy of the load forecasts, such as data quality, goodness of the underlying model and load composition. Due to the strong correlation between the input variables (e.g., weather and calendar variables) and the load, the quality of input data plays a vital role in forecasting practices. Even if the forecasting model were able to capture most of the salient features of the load, a low quality input data may result in inaccurate forecasts. Most of the data cleansing efforts in the load forecasting literature have been devoted to the load data. Few studies focused on weather data cleansing for load forecasting. This research proposes an anomaly detection method for the temperature data. The method consists of two components: a load-based temperature prediction model and a detection technique. The effectiveness of the proposed method is demonstrated through two case studies: one based on the data from the Global Energy Forecasting Competition 2014, and the other based on the data published by ISO New England. The results show that by removing the detected observations from the original input data, the final load forecast accuracy is enhanced.

Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, themore » proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.« less

Carbon-Carbon Recuperators in Closed-Brayton-Cycle Space Power Systems

NASA Technical Reports Server (NTRS)

Barrett, Michael J.; Johnson, Paul K.

2006-01-01

The use of carbon-carbon (C-C) recuperators in closed-Brayton-cycle space power conversion systems was assessed. Recuperator performance was forecast based on notional thermodynamic cycle state values for planetary missions. Resulting thermal performance, mass and volume for plate-fin C-C recuperators were estimated and quantitatively compared with values for conventional offset-strip-fin metallic designs. Mass savings of 40-55% were projected for C-C recuperators with effectiveness greater than 0.9 and thermal loads from 25-1400 kWt. The smaller thermal loads corresponded with lower mass savings; however, at least 50% savings were forecast for all loads above 300 kWt. System-related material challenges and compatibility issues were also discussed.

Bulk electric system reliability evaluation incorporating wind power and demand side management

NASA Astrophysics Data System (ADS)

Huang, Dange

Electric power systems are experiencing dramatic changes with respect to structure, operation and regulation and are facing increasing pressure due to environmental and societal constraints. Bulk electric system reliability is an important consideration in power system planning, design and operation particularly in the new competitive environment. A wide range of methods have been developed to perform bulk electric system reliability evaluation. Theoretically, sequential Monte Carlo simulation can include all aspects and contingencies in a power system and can be used to produce an informative set of reliability indices. It has become a practical and viable tool for large system reliability assessment technique due to the development of computing power and is used in the studies described in this thesis. The well-being approach used in this research provides the opportunity to integrate an accepted deterministic criterion into a probabilistic framework. This research work includes the investigation of important factors that impact bulk electric system adequacy evaluation and security constrained adequacy assessment using the well-being analysis framework. Load forecast uncertainty is an important consideration in an electrical power system. This research includes load forecast uncertainty considerations in bulk electric system reliability assessment and the effects on system, load point and well-being indices and reliability index probability distributions are examined. There has been increasing worldwide interest in the utilization of wind power as a renewable energy source over the last two decades due to enhanced public awareness of the environment. Increasing penetration of wind power has significant impacts on power system reliability, and security analyses become more uncertain due to the unpredictable nature of wind power. The effects of wind power additions in generating and bulk electric system reliability assessment considering site wind speed correlations and the interactive effects of wind power and load forecast uncertainty on system reliability are examined. The concept of the security cost associated with operating in the marginal state in the well-being framework is incorporated in the economic analyses associated with system expansion planning including wind power and load forecast uncertainty. Overall reliability cost/worth analyses including security cost concepts are applied to select an optimal wind power injection strategy in a bulk electric system. The effects of the various demand side management measures on system reliability are illustrated using the system, load point, and well-being indices, and the reliability index probability distributions. The reliability effects of demand side management procedures in a bulk electric system including wind power and load forecast uncertainty considerations are also investigated. The system reliability effects due to specific demand side management programs are quantified and examined in terms of their reliability benefits.

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mbamalu, G.A.N.; El-Hawary, M.E.

The authors propose suboptimal least squares or IRWLS procedures for estimating the parameters of a seasonal multiplicative AR model encountered during power system load forecasting. The proposed method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squaresmore » or the IRWLS to estimate the optimal season AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method.« less

An econometric simulation model of income and electricity demand in Alaska's Railbelt, 1982-2022

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maddigan, R.J.; Hill, L.J.; Hamblin, D.M.

1987-01-01

This report describes the specification of-and forecasts derived from-the Alaska Railbelt Electricity Load, Macroeconomic (ARELM) model. ARELM was developed as an independent, modeling tool for the evaluation of the need for power from the Susitna Hydroelectric Project which has been proposed by the Alaska Power Authority. ARELM is an econometric simulation model consisting of 61 equations - 46 behavioral equations and 15 identities. The system includes two components: (1) ARELM-MACRO which is a system of equations that simulates the performance of both the total Alaskan and Railbelt macroeconomies and (2) ARELM-LOAD which projects electricity-related activity in the Alaskan Railbelt region.more » The modeling system is block recursive in the sense that forecasts of population, personal income, and employment in the Railbelt derived from ARELM-MACRO are used as explanatory variables in ARELM-LOAD to simulate electricity demand, the real average price of electricity, and the number of customers in the Railbelt. Three scenarios based on assumptions about the future price of crude oil are simulated and documented in the report. The simulations, which do not include the cost-of-power impacts of Susitna-based generation, show that the growth rate in Railbelt electricity load is between 2.5 and 2.7% over the 1982 to 2022 forecast period. The forecasting results are consistent with other projections of load growth in the region using different modeling approaches.« less

7 CFR 1710.209 - Approval requirements for load forecast work plans.

Code of Federal Regulations, 2010 CFR

2010-01-01

... cooperate in the preparation of and submittal of the load forecast work plan of their power supply borrower. (b) An approved load forecast work plan establishes the process for the preparation and maintenance... approved load forecast work plan must outline the coordination and preparation requirements for both the...

Neural network based short-term load forecasting using weather compensation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chow, T.W.S.; Leung, C.T.

This paper presents a novel technique for electric load forecasting based on neural weather compensation. The proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. The weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error.

A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

PubMed Central

Moriano, Javier; Rodríguez, Francisco Javier; Martín, Pedro; Jiménez, Jose Antonio; Vuksanovic, Branislav

2016-01-01

In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF) allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected. PMID:26771613

Low Probability Tail Event Analysis and Mitigation in BPA Control Area: Task One Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Shuai; Makarov, Yuri V.

This is a report for task one of the tail event analysis project for BPA. Tail event refers to the situation in a power system when unfavorable forecast errors of load and wind are superposed onto fast load and wind ramps, or non-wind generators falling short of scheduled output, the imbalance between generation and load becomes very significant. This type of events occurs infrequently and appears on the tails of the distribution of system power imbalance; therefore, is referred to as tail events. This report analyzes what happened during the Electric Reliability Council of Texas (ERCOT) reliability event on Februarymore » 26, 2008, which was widely reported because of the involvement of wind generation. The objective is to identify sources of the problem, solutions to it and potential improvements that can be made to the system. Lessons learned from the analysis include the following: (1) Large mismatch between generation and load can be caused by load forecast error, wind forecast error and generation scheduling control error on traditional generators, or a combination of all of the above; (2) The capability of system balancing resources should be evaluated both in capacity (MW) and in ramp rate (MW/min), and be procured accordingly to meet both requirements. The resources need to be able to cover a range corresponding to the variability of load and wind in the system, additional to other uncertainties; (3) Unexpected ramps caused by load and wind can both become the cause leading to serious issues; (4) A look-ahead tool evaluating system balancing requirement during real-time operations and comparing that with available system resources should be very helpful to system operators in predicting the forthcoming of similar events and planning ahead; and (5) Demand response (only load reduction in ERCOT event) can effectively reduce load-generation mismatch and terminate frequency deviation in an emergency situation.« less

Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

PubMed Central

Hu, Zhongyi; Xiong, Tao

2013-01-01

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature. PMID:24459425

Electricity load forecasting using support vector regression with memetic algorithms.

PubMed

Hu, Zhongyi; Bao, Yukun; Xiong, Tao

2013-01-01

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.

Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus

NASA Technical Reports Server (NTRS)

Momoh, James; Chattopadhyay, Deb; Elfayoumy, Mahmoud

1996-01-01

The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the GP model which in turn produces the pre-schedule of the optimal control model. Some preliminary results based on a hypothetical test case will be presented for the load forecasting module. The computer codes for the three modules will be made available fe adoption by bus operating agencies. Sample results will be provided using these models. The software will be a useful tool for supporting the control systems for the Electric Bus project of NASA.

Carbon-Carbon Recuperators in Closed-Brayton-Cycle Space Power Systems

NASA Technical Reports Server (NTRS)

Barrett, Michael J.; Johnson, Paul K.; Naples, Andrew G.

2006-01-01

The feasibility of using carbon-carbon (C-C) recuperators in conceptual closed-Brayton-cycle space power conversion systems was assessed. Recuperator performance expectations were forecast based on notional thermodynamic cycle state values for potential planetary missions. Resulting thermal performance, mass and volume for plate-fin C-C recuperators were estimated and quantitatively compared with values for conventional offset-strip-fin metallic designs. Mass savings of 30 to 60 percent were projected for C-C recuperators with effectiveness greater than 0.9 and thermal loads from 25 to 1400 kWt. The smaller thermal loads corresponded with lower mass savings; however, 60 percent savings were forecast for all loads above 300 kWt. System-related material challenges and compatibility issues were also discussed.

Electricity forecasting on the individual household level enhanced based on activity patterns

PubMed Central

Gajowniczek, Krzysztof; Ząbkowski, Tomasz

2017-01-01

Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents’ daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken. PMID:28423039

Electricity forecasting on the individual household level enhanced based on activity patterns.

PubMed

Gajowniczek, Krzysztof; Ząbkowski, Tomasz

2017-01-01

Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents' daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE PAGES

Buitrago, Jaime; Asfour, Shihab

2017-01-01

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buitrago, Jaime; Asfour, Shihab

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Load Forecasting in Electric Utility Integrated Resource Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carvallo, Juan Pablo; Larsen, Peter H.; Sanstad, Alan H

Integrated resource planning (IRP) is a process used by many vertically-integrated U.S. electric utilities to determine least-cost/risk supply and demand-side resources that meet government policy objectives and future obligations to customers and, in many cases, shareholders. Forecasts of energy and peak demand are a critical component of the IRP process. There have been few, if any, quantitative studies of IRP long-run (planning horizons of two decades) load forecast performance and its relationship to resource planning and actual procurement decisions. In this paper, we evaluate load forecasting methods, assumptions, and outcomes for 12 Western U.S. utilities by examining and comparing plansmore » filed in the early 2000s against recent plans, up to year 2014. We find a convergence in the methods and data sources used. We also find that forecasts in more recent IRPs generally took account of new information, but that there continued to be a systematic over-estimation of load growth rates during the period studied. We compare planned and procured resource expansion against customer load and year-to-year load growth rates, but do not find a direct relationship. Load sensitivities performed in resource plans do not appear to be related to later procurement strategies even in the presence of large forecast errors. These findings suggest that resource procurement decisions may be driven by other factors than customer load growth. Our results have important implications for the integrated resource planning process, namely that load forecast accuracy may not be as important for resource procurement as is generally believed, that load forecast sensitivities could be used to improve the procurement process, and that management of load uncertainty should be prioritized over more complex forecasting techniques.« less

Data-driven forecasting algorithms for building energy consumption

NASA Astrophysics Data System (ADS)

Noh, Hae Young; Rajagopal, Ram

2013-04-01

This paper introduces two forecasting methods for building energy consumption data that are recorded from smart meters in high resolution. For utility companies, it is important to reliably forecast the aggregate consumption profile to determine energy supply for the next day and prevent any crisis. The proposed methods involve forecasting individual load on the basis of their measurement history and weather data without using complicated models of building system. The first method is most efficient for a very short-term prediction, such as the prediction period of one hour, and uses a simple adaptive time-series model. For a longer-term prediction, a nonparametric Gaussian process has been applied to forecast the load profiles and their uncertainty bounds to predict a day-ahead. These methods are computationally simple and adaptive and thus suitable for analyzing a large set of data whose pattern changes over the time. These forecasting methods are applied to several sets of building energy consumption data for lighting and heating-ventilation-air-conditioning (HVAC) systems collected from a campus building at Stanford University. The measurements are collected every minute, and corresponding weather data are provided hourly. The results show that the proposed algorithms can predict those energy consumption data with high accuracy.

A temperature match based optimization method for daily load prediction considering DLC effect

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Z.

This paper presents a unique optimization method for short term load forecasting. The new method is based on the optimal template temperature match between the future and past temperatures. The optimal error reduction technique is a new concept introduced in this paper. Two case studies show that for hourly load forecasting, this method can yield results as good as the rather complicated Box-Jenkins Transfer Function method, and better than the Box-Jenkins method; for peak load prediction, this method is comparable in accuracy to the neural network method with back propagation, and can produce more accurate results than the multi-linear regressionmore » method. The DLC effect on system load is also considered in this method.« less

Short-term Power Load Forecasting Based on Balanced KNN

NASA Astrophysics Data System (ADS)

Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei

2018-03-01

To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-07-25

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load valuesmore » to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

NASA Astrophysics Data System (ADS)

Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vectormore » regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.« less

Long term load forecasting accuracy in electric utility integrated resource planning

DOE PAGES

Carvallo, Juan Pablo; Larsen, Peter H.; Sanstad, Alan H.; ...

2018-05-23

Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electricmore » utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. As a result, we review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracy and its underlying causes.« less

Long term load forecasting accuracy in electric utility integrated resource planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carvallo, Juan Pablo; Larsen, Peter H.; Sanstad, Alan H.

Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electricmore » utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. As a result, we review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracy and its underlying causes.« less

Study on load forecasting to data centers of high power density based on power usage effectiveness

NASA Astrophysics Data System (ADS)

Zhou, C. C.; Zhang, F.; Yuan, Z.; Zhou, L. M.; Wang, F. M.; Li, W.; Yang, J. H.

2016-08-01

There is usually considerable energy consumption in data centers. Load forecasting to data centers is in favor of formulating regional load density indexes and of great benefit to getting regional spatial load forecasting more accurately. The building structure and the other influential factors, i.e. equipment, geographic and climatic conditions, are considered for the data centers, and a method to forecast the load of the data centers based on power usage effectiveness is proposed. The cooling capacity of a data center and the index of the power usage effectiveness are used to forecast the power load of the data center in the method. The cooling capacity is obtained by calculating the heat load of the data center. The index is estimated using the group decision-making method of mixed language information. An example is given to prove the applicability and accuracy of this method.

7 CFR 1710.300 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... forecast. The forecast should be used by the board of directors and the manager to guide the system towards... projected results of future actions planned by the borrower's board of directors; (2) The financial goals... type of large power loads, projections of future borrowings and the associated interest, projected...

7 CFR 1710.300 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... forecast. The forecast should be used by the board of directors and the manager to guide the system towards... projected results of future actions planned by the borrower's board of directors; (2) The financial goals... type of large power loads, projections of future borrowings and the associated interest, projected...

Automatic load forecasting. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, D.J.; Vemuri, S.

A method which lends itself to on-line forecasting of hourly electric loads is presented and the results of its use are compared to models developed using the Box-Jenkins method. The method consists of processing the historical hourly loads with a sequential least-squares estimator to identify a finite order autoregressive model which in turn is used to obtain a parsimonious autoregressive-moving average model. A procedure is also defined for incorporating temperature as a variable to improve forecasts where loads are temperature dependent. The method presented has several advantages in comparison to the Box-Jenkins method including much less human intervention and improvedmore » model identification. The method has been tested using three-hourly data from the Lincoln Electric System, Lincoln, Nebraska. In the exhaustive analyses performed on this data base this method produced significantly better results than the Box-Jenkins method. The method also proved to be more robust in that greater confidence could be placed in the accuracy of models based upon the various measures available at the identification stage.« less

A Solar Time-Based Analog Ensemble Method for Regional Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Zhang, Xinmin; Li, Yuan

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, formore » both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.« less

Traffic-load forecasting using weigh-in-motion data

DOT National Transportation Integrated Search

1997-03-01

Vehicular traffic loading is a crucial consideration for the design and maintenance of pavements. With the help of weigh-in-motion (WIM) systems, the information about date, time, speed, lane of travel, lateral lane position, axle spacing, and wheel ...

Steam-load-forecasting technique for central-heating plants. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, M.C.; Carnahan, J.V.

Because boilers generally are most efficient at full loads, the Army could achieve significant savings by running fewer boilers at high loads rather than more boilers at low loads. A reliable load prediction technique could help ensure that only those boilers required to meet demand are on line. This report presents the results of an investigation into the feasibility of forecasting heat plant steam loads from historical patterns and weather information. Using steam flow data collected at Fort Benjamin Harrison, IN, a Box-Jenkins transfer function model with an acceptably small prediction error was initially identified. Initial investigation of forecast modelmore » development appeared successful. Dynamic regression methods using actual ambient temperatures yielded the best results. Box-Jenkins univariate models' results appeared slightly less accurate. Since temperature information was not needed for model building and forecasting, however, it is recommended that Box-Jenkins models be considered prime candidates for load forecasting due to their simpler mathematics.« less

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Minimum approval requirements for all load forecasts. 1710.205 Section 1710.205 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load Forecasts §...

Forecasting staffing needs for productivity management in hospital laboratories.

PubMed

Pang, C Y; Swint, J M

1985-12-01

Daily and weekly prediction models are developed to help forecast hospital laboratory work load for the entire laboratory and individual sections of the laboratory. The models are tested using historical data obtained from hospital census and laboratory log books of a 90-bed southwestern hospital. The results indicate that the predictor variables account for 50%, 81%, 56%, and 82% of the daily work load variation for chemistry, hematology, and microbiology sections, and for the entire laboratory, respectively. Equivalent results for the weekly model are 53%, 72%, 12%, and 78% for the same respective sections. On the basis of the predicted work load, staffing assessment is made and a productivity monitoring system constructed. The purpose of such a system is to assist laboratory management in efforts to utilize laboratory manpower in a more efficient and cost-effective manner.

7 CFR 1710.209 - Approval requirements for load forecast work plans.

Code of Federal Regulations, 2011 CFR

2011-01-01

...) In addition to the approved load forecast required under §§ 1710.202 and 1710.203, any power supply... that are members of a power supply borrower with a total utility plant of $500 million or more must cooperate in the preparation of and submittal of the load forecast work plan of their power supply borrower...

Clarus multi-state regional demonstrations, evaluation of use case #2 : seasonal load restriction tool.

DOT National Transportation Integrated Search

2011-07-01

This report presents the results of an evaluation of the demonstration of an experimental seasonal load restriction decision support tool. This system offers state DOTs subsurface condition forecasts (such as moisture, temperature, and freeze-thaw tr...

Compensated Box-Jenkins transfer function for short term load forecast

DOE Office of Scientific and Technical Information (OSTI.GOV)

Breipohl, A.; Yu, Z.; Lee, F.N.

In the past years, the Box-Jenkins ARIMA method and the Box-Jenkins transfer function method (BJTF) have been among the most commonly used methods for short term electrical load forecasting. But when there exists a sudden change in the temperature, both methods tend to exhibit larger errors in the forecast. This paper demonstrates that the load forecasting errors resulting from either the BJ ARIMA model or the BJTF model are not simply white noise, but rather well-patterned noise, and the patterns in the noise can be used to improve the forecasts. Thus a compensated Box-Jenkins transfer method (CBJTF) is proposed tomore » improve the accuracy of the load prediction. Some case studies have been made which result in about a 14-33% reduction of the root mean square (RMS) errors of the forecasts, depending on the compensation time period as well as the compensation method used.« less

Load research manual. Volume 2: Fundamentals of implementing load research procedures

NASA Astrophysics Data System (ADS)

1980-11-01

This manual will assist electric utilities and state regulatory authorities in investigating customer electricity demand as part of cost-of-service studies, rate design, marketing research, system design, load forecasting, rate reform analysis, and load management research. Load research procedures are described in detail. Research programs at three utilities are compared: Carolina Power and Light Company, Long Island Lighting Company, and Southern California Edison Company. A load research bibliography and glossaries of load research and statistical terms are also included.

7 CFR 1710.202 - Requirement to prepare a load forecast-power supply borrowers.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 11 2011-01-01 2011-01-01 false Requirement to prepare a load forecast-power supply...—power supply borrowers. (a) A power supply borrower with a total utility plant of $500 million or more... be prepared pursuant to the approved load forecast work plan. (b) A power supply borrower that is a...

Load forecast method of electric vehicle charging station using SVR based on GA-PSO

NASA Astrophysics Data System (ADS)

Lu, Kuan; Sun, Wenxue; Ma, Changhui; Yang, Shenquan; Zhu, Zijian; Zhao, Pengfei; Zhao, Xin; Xu, Nan

2017-06-01

This paper presents a Support Vector Regression (SVR) method for electric vehicle (EV) charging station load forecast based on genetic algorithm (GA) and particle swarm optimization (PSO). Fuzzy C-Means (FCM) clustering is used to establish similar day samples. GA is used for global parameter searching and PSO is used for a more accurately local searching. Load forecast is then regressed using SVR. The practical load data of an EV charging station were taken to illustrate the proposed method. The result indicates an obvious improvement in the forecasting accuracy compared with SVRs based on PSO and GA exclusively.

Hybrid robust predictive optimization method of power system dispatch

DOEpatents

Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY

2011-08-02

A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

[Demography perspectives and forecasts of the demand for electricity].

PubMed

Roy, L; Guimond, E

1995-01-01

"Demographic perspectives form an integral part in the development of electric load forecasts. These forecasts in turn are used to justify the addition and repair of generating facilities that will supply power in the coming decades. The goal of this article is to present how demographic perspectives are incorporated into the electric load forecasting in Quebec. The first part presents the methods, hypotheses and results of population and household projections used by Hydro-Quebec in updating its latest development plan. The second section demonstrates applications of such demographic projections for forecasting the electric load, with a focus on the residential sector." (SUMMARY IN ENG AND SPA) excerpt

Studies of air traffic forecasts, airspace load and the effect of ADS-B via satellites on flight times

NASA Astrophysics Data System (ADS)

Zhong, Z. W.; Ridhwan Salleh, Saiful; Chow, W. X.; Ong, Z. M.

2016-10-01

Air traffic forecasting is important as it helps stakeholders to plan their budgets and facilities. Thus, three most commonly used forecasting models were compared to see which model suited the air passenger traffic the best. General forecasting equations were also created to forecast the passenger traffic. The equations could forecast around 6.0% growth from 2015 onwards. Another study sought to provide an initial work for determining a theoretical airspace load with relevant calculations. The air traffic was simulated to investigate the current airspace load. Logical and reasonable results were obtained from the modelling and simulations. The current utilization percentages for airspace load per hour and the static airspace load in the interested airspace were found to be 6.64% and 11.21% respectively. Our research also studied how ADS-B would affect the time taken for aircraft to travel. 6000 flights departing from and landing at the airport were studied. New flight plans were simulated with improved flight paths due to the implementation of ADS-B, and flight times of all studied flights could be improved.

7 CFR 1710.207 - RUS criteria for approval of load forecasts by distribution borrowers not required to maintain an...

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false RUS criteria for approval of load forecasts by distribution borrowers not required to maintain an approved load forecast on an ongoing basis. 1710.207 Section 1710.207 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PR...

Ramping and Uncertainty Prediction Tool - Analysis and Visualization of Wind Generation Impact on Electrical Grid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etingov, Pavel; Makarov, PNNL Yuri; Subbarao, PNNL Kris

RUT software is designed for use by the Balancing Authorities to predict and display additional requirements caused by the variability and uncertainty in load and generation. The prediction is made for the next operating hours as well as for the next day. The tool predicts possible deficiencies in generation capability and ramping capability. This deficiency of balancing resources can cause serious risks to power system stability and also impact real-time market energy prices. The tool dynamically and adaptively correlates changing system conditions with the additional balancing needs triggered by the interplay between forecasted and actual load and output of variablemore » resources. The assessment is performed using a specially developed probabilistic algorithm incorporating multiple sources of uncertainty including wind, solar and load forecast errors. The tool evaluates required generation for a worst case scenario, with a user-specified confidence level.« less

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abbas, Nikhar; Tom, Nathan M

2017-06-03

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abbas, Nikhar; Tom, Nathan

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Energy management of a university campus utilizing short-term load forecasting with an artificial neural network

NASA Astrophysics Data System (ADS)

Palchak, David

Electrical load forecasting is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term load forecast is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.

76 FR 72203 - Voltage Coordination on High Voltage Grids; Notice of Reliability Workshop Agenda

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-22

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. AD12-5-000] Voltage... currently coordinate the dispatch of reactive resources to support forecasted loads, generation and... reactive power needs of the distribution system or loads are coordinated or optimized. Panelists: Khaled...

Low Probability Tail Event Analysis and Mitigation in BPA Control Area: Task 2 Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Shuai; Makarov, Yuri V.; McKinstry, Craig A.

Task report detailing low probability tail event analysis and mitigation in BPA control area. Tail event refers to the situation in a power system when unfavorable forecast errors of load and wind are superposed onto fast load and wind ramps, or non-wind generators falling short of scheduled output, causing the imbalance between generation and load to become very significant.

Controller for thermostatically controlled loads

DOEpatents

Lu, Ning; Zhang, Yu; Du, Pengwei; Makarov, Yuri V.

2016-06-07

A system and method of controlling aggregated thermostatically controlled appliances (TCAs) for demand response is disclosed. A targeted load profile is formulated and a forecasted load profile is generated. The TCAs within an "on" or "off" control group are prioritized based on their operating temperatures. The "on" or "off" status of the TCAs is determined. Command signals are sent to turn on or turn off the TCAs.

The transport forecast - an important stage of transport management

NASA Astrophysics Data System (ADS)

Dragu, Vasile; Dinu, Oana; Oprea, Cristina; Alina Roman, Eugenia

2017-10-01

The transport system is a powerful system with varying loads in operation coming from changes in freight and passenger traffic in different time periods. The variations are due to the specific conditions of organization and development of socio-economic activities. The causes of varying loads can be included in three groups: economic, technical and organizational. The assessing of transport demand variability leads to proper forecast and development of the transport system, knowing that the market price is determined on equilibrium between supply and demand. The reduction of transport demand variability through different technical solutions, organizational, administrative, legislative leads to an increase in the efficiency and effectiveness of transport. The paper presents a new way of assessing the future needs of transport through dynamic series. Both researchers and practitioners in transport planning can benefit from the research results. This paper aims to analyze in an original approach how a good transport forecast can lead to a better management in transport, with significant effects on transport demand full meeting in quality terms. The case study shows how dynamic series of statistics can be used to identify the size of future demand addressed to the transport system.

Wet snow hazard for power lines: a forecast and alert system applied in Italy

NASA Astrophysics Data System (ADS)

Bonelli, P.; Lacavalla, M.; Marcacci, P.; Mariani, G.; Stella, G.

2011-09-01

Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly. The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2010 CFR

2010-01-01

... narrative shall address the overall approach, time periods, and expected internal and external uses of the forecast. Examples of internal uses include providing information for developing or monitoring demand side... suppliers. Examples of external uses include meeting state and Federal regulatory requirements, obtaining...

Performance of fuzzy approach in Malaysia short-term electricity load forecasting

NASA Astrophysics Data System (ADS)

Mansor, Rosnalini; Zulkifli, Malina; Yusof, Muhammad Mat; Ismail, Mohd Isfahani; Ismail, Suzilah; Yin, Yip Chee

2014-12-01

Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity load forecasting is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity load forecasting. Being a multi culture country Malaysia has many major festive celebrations such as Eidul Fitri, Chinese New Year and Deepavali but they are moving holidays due to non-fixed dates on the Gregorian calendar. This study emphasis on the performance of fuzzy approach in forecasting electricity load when considering the presence of moving holidays. Autoregressive Distributed Lag model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity load. The result indicated that day types, public holidays and several lags of electricity load were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules.

Operational wave now- and forecast in the German Bight as a basis for the assessment of wave-induced hydrodynamic loads on coastal dikes

NASA Astrophysics Data System (ADS)

Dreier, Norman; Fröhle, Peter

2017-12-01

The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based early warning system. As a basis for the assessment of the wave-induced hydrodynamic loads, an operational wave now- and forecast system is set up that consists of i) available field measurements from the federal and local authorities and ii) data from numerical simulation of waves in the German Bight using the SWAN wave model. In this study, results of the hindcast of deep water wave conditions during the winter storm on 5-6 December, 2013 (German name `Xaver') are shown and compared with available measurements. Moreover field measurements of wave run-up from the local authorities at a sea dike on the German North Sea Island of Pellworm are presented and compared against calculated wave run-up using the EurOtop (2016) approach.

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2012 CFR

2012-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2014 CFR

2014-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2011 CFR

2011-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2013 CFR

2013-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2013 CFR

2013-01-01

... electronically to RUS computer software applications. RUS will evaluate borrower load forecasts for readability...'s engineering planning documents, such as the construction work plan, incorporate consumer and usage...

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2011 CFR

2011-01-01

... electronically to RUS computer software applications. RUS will evaluate borrower load forecasts for readability...'s engineering planning documents, such as the construction work plan, incorporate consumer and usage...

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2012 CFR

2012-01-01

... electronically to RUS computer software applications. RUS will evaluate borrower load forecasts for readability...'s engineering planning documents, such as the construction work plan, incorporate consumer and usage...

Daily Peak Load Forecasting of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data

NASA Astrophysics Data System (ADS)

Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki

By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to forecast the movement of the electric power demand carefully in advance. And using that forecast as the source, supply and demand management plan should be made. Thus load forecasting is said to be an important job among demand investment of electric power companies. So far, forecasting method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of forecasting accuracy. Those forecasting accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new forecasting method with higher accuracy is needed. In this paper, to develop the forecasting accuracy of the former methods, the daily peak load forecasting method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.

7 CFR 1710.152 - Primary support documents.

Code of Federal Regulations, 2010 CFR

2010-01-01

... capital investments required to serve a borrower's planned new loads, improve service reliability and... manager to guide the system toward its financial goals. The forecast submitted in support of a loan...

Modeling and Analysis of Commercial Building Electrical Loads for Demand Side Management

NASA Astrophysics Data System (ADS)

Berardino, Jonathan

In recent years there has been a push in the electric power industry for more customer involvement in the electricity markets. Traditionally the end user has played a passive role in the planning and operation of the power grid. However, many energy markets have begun opening up opportunities to consumers who wish to commit a certain amount of their electrical load under various demand side management programs. The potential benefits of more demand participation include reduced operating costs and new revenue opportunities for the consumer, as well as more reliable and secure operations for the utilities. The management of these load resources creates challenges and opportunities to the end user that were not present in previous market structures. This work examines the behavior of commercial-type building electrical loads and their capacity for supporting demand side management actions. This work is motivated by the need for accurate and dynamic tools to aid in the advancement of demand side operations. A dynamic load model is proposed for capturing the response of controllable building loads. Building-specific load forecasting techniques are developed, with particular focus paid to the integration of building management system (BMS) information. These approaches are tested using Drexel University building data. The application of building-specific load forecasts and dynamic load modeling to the optimal scheduling of multi-building systems in the energy market is proposed. Sources of potential load uncertainty are introduced in the proposed energy management problem formulation in order to investigate the impact on the resulting load schedule.

Optimize Short Term load Forcasting Anomalous Based Feed Forward Backpropagation

NASA Astrophysics Data System (ADS)

Mulyadi, Y.; Abdullah, A. G.; Rohmah, K. A.

2017-03-01

This paper contains the Short-Term Load Forecasting (STLF) using artificial neural network especially feed forward back propagation algorithm which is particularly optimized in order to getting a reduced error value result. Electrical load forecasting target is a holiday that hasn’t identical pattern and different from weekday’s pattern, in other words the pattern of holiday load is an anomalous. Under these conditions, the level of forecasting accuracy will be decrease. Hence we need a method that capable to reducing error value in anomalous load forecasting. Learning process of algorithm is supervised or controlled, then some parameters are arranged before performing computation process. Momentum constant a value is set at 0.8 which serve as a reference because it has the greatest converge tendency. Learning rate selection is made up to 2 decimal digits. In addition, hidden layer and input component are tested in several variation of number also. The test result leads to the conclusion that the number of hidden layer impact on the forecasting accuracy and test duration determined by the number of iterations when performing input data until it reaches the maximum of a parameter value.

Youth temperament, harsh parenting, and variation in the oxytocin receptor gene forecast allostatic load during emerging adulthood.

PubMed

Brody, Gene H; Yu, Tianyi; Barton, Allen W; Miller, Gregory E; Chen, Edith

2017-08-01

An association has been found between receipt of harsh parenting in childhood and adult health problems. However, this research has been principally retrospective, has treated children as passive recipients of parental behavior, and has overlooked individual differences in youth responsivity to harsh parenting. In a 10-year multiple-wave prospective study of African American families, we addressed these issues by focusing on the influence of polymorphisms in the oxytocin receptor gene (OXTR), variants of which appear to buffer or amplify responses to environmental stress. The participants were 303 youths, with a mean age of 11.2 at the first assessment, and their parents, all of whom were genotyped for variations in the rs53576 (A/G) polymorphism. Teachers rated preadolescent (ages 11 to 13) emotionally intense and distractible temperaments, and adolescents (ages 15 and 16) reported receipt of harsh parenting. Allostatic load was assessed during young adulthood (ages 20 and 21). Difficult preadolescent temperament forecast elevated receipt of harsh parenting in adolescence, and adolescents who experienced harsh parenting evinced high allostatic load during young adulthood. However, these associations emerged only among children and parents who carried A alleles of the OXTR genotype. The results suggest the oxytocin system operates along with temperament and parenting to forecast young adults' allostatic load.

7 CFR 1710.203 - Requirement to prepare a load forecast-distribution borrowers.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Requirement to prepare a load forecast-distribution borrowers. 1710.203 Section 1710.203 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load...

The time series approach to short term load forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hagan, M.T.; Behr, S.M.

The application of time series analysis methods to load forecasting is reviewed. It is shown than Box and Jenkins time series models, in particular, are well suited to this application. The logical and organized procedures for model development using the autocorrelation function make these models particularly attractive. One of the drawbacks of these models is the inability to accurately represent the nonlinear relationship between load and temperature. A simple procedure for overcoming this difficulty is introduced, and several Box and Jenkins models are compared with a forecasting procedure currently used by a utility company.

Using Forecasting to Predict Long-Term Resource Utilization for Web Services

ERIC Educational Resources Information Center

Yoas, Daniel W.

2013-01-01

Researchers have spent years understanding resource utilization to improve scheduling, load balancing, and system management through short-term prediction of resource utilization. Early research focused primarily on single operating systems; later, interest shifted to distributed systems and, finally, into web services. In each case researchers…

Application of global weather and climate model output to the design and operation of wind-energy systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curry, Judith

This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less

Toward quantitative forecasts of volcanic ash dispersal: Using satellite retrievals for optimal estimation of source terms

NASA Astrophysics Data System (ADS)

Zidikheri, Meelis J.; Lucas, Christopher; Potts, Rodney J.

2017-08-01

Airborne volcanic ash is a hazard to aviation. There is an increasing demand for quantitative forecasts of ash properties such as ash mass load to allow airline operators to better manage the risks of flying through airspace likely to be contaminated by ash. In this paper we show how satellite-derived mass load information at times prior to the issuance of the latest forecast can be used to estimate various model parameters that are not easily obtained by other means such as the distribution of mass of the ash column at the volcano. This in turn leads to better forecasts of ash mass load. We demonstrate the efficacy of this approach using several case studies.

Loss of Load Probability Calculation for West Java Power System with Nuclear Power Plant Scenario

NASA Astrophysics Data System (ADS)

Azizah, I. D.; Abdullah, A. G.; Purnama, W.; Nandiyanto, A. B. D.; Shafii, M. A.

2017-03-01

Loss of Load Probability (LOLP) index showing the quality and performance of an electrical system. LOLP value is affected by load growth, the load duration curve, forced outage rate of the plant, number and capacity of generating units. This reliability index calculation begins with load forecasting to 2018 using multiple regression method. Scenario 1 with compositions of conventional plants produce the largest LOLP in 2017 amounted to 71.609 days / year. While the best reliability index generated in scenario 2 with the NPP amounted to 6.941 days / year in 2015. Improved reliability of systems using nuclear power more efficiently when compared to conventional plants because it also has advantages such as emission-free, inexpensive fuel costs, as well as high level of plant availability.

7 CFR 1710.202 - Requirement to prepare a load forecast-power supply borrowers.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Requirement to prepare a load forecast-power supply borrowers. 1710.202 Section 1710.202 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load...

Development of personal pollen information—the next generation of pollen information and a step forward for hay fever sufferers

NASA Astrophysics Data System (ADS)

Kmenta, Maximilian; Bastl, Katharina; Jäger, Siegfried; Berger, Uwe

2014-10-01

Pollen allergies affect a large part of the European population and are considered likely to increase. User feedback indicates that there are difficulties in providing proper information and valid forecasts using traditional methods of aerobiology due to a variety of factors. Allergen content, pollen loads, and pollen allergy symptoms vary per region and year. The first steps in challenging such issues have already been undertaken. A personalized pollen-related symptom forecast is thought to be a possible answer. However, attempts made thus far have not led to an improvement in daily forecasting procedures. This study describes a model that was launched in 2013 in Austria to provide the first available personal pollen information. This system includes innovative forecast models using bi-hourly pollen data, traditional pollen forecasts based on historical data, meteorological data, and recent symptom data from the patient's hayfever diary. Furthermore, it calculates the personal symptom load in real time, in particular, the entries of the previous 5 days, to classify users. The personal pollen information was made available in Austria on the Austrian pollen information website and via a mobile pollen application, described herein for the first time. It is supposed that the inclusion of personal symptoms will lead to major improvements in pollen information concerning hay fever sufferers.

Analysis of recurrent neural networks for short-term energy load forecasting

NASA Astrophysics Data System (ADS)

Di Persio, Luca; Honchar, Oleksandr

2017-11-01

Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.

Linked Hydrologic-Hydrodynamic Model Framework to Forecast Impacts of Rivers on Beach Water Quality

NASA Astrophysics Data System (ADS)

Anderson, E. J.; Fry, L. M.; Kramer, E.; Ritzenthaler, A.

2014-12-01

The goal of NOAA's beach quality forecasting program is to use a multi-faceted approach to aid in detection and prediction of bacteria in recreational waters. In particular, our focus has been on the connection between tributary loads and bacteria concentrations at nearby beaches. While there is a clear link between stormwater runoff and beach water quality, quantifying the contribution of river loadings to nearshore bacterial concentrations is complicated due to multiple processes that drive bacterial concentrations in rivers as well as those processes affecting the fate and transport of bacteria upon exiting the rivers. In order to forecast potential impacts of rivers on beach water quality, we developed a linked hydrologic-hydrodynamic water quality framework that simulates accumulation and washoff of bacteria from the landscape, and then predicts the fate and transport of washed off bacteria from the watershed to the coastal zone. The framework includes a watershed model (IHACRES) to predict fecal indicator bacteria (FIB) loadings to the coastal environment (accumulation, wash-off, die-off) as a function of effective rainfall. These loadings are input into a coastal hydrodynamic model (FVCOM), including a bacteria transport model (Lagrangian particle), to simulate 3D bacteria transport within the coastal environment. This modeling system provides predictive tools to assist local managers in decision-making to reduce human health threats.

Operational Planning of Channel Airlift Missions Using Forecasted Demand

DTIC Science & Technology

2013-03-01

tailored to the specific problem ( Metaheuristics , 2005). As seen in the section Cargo Loading Algorithm , heuristic methods are often iterative...that are equivalent to the forecasted cargo amount. The simulated pallets are then used in a heuristic cargo loading algorithm . The loading... algorithm places cargo onto available aircraft (based on real schedules) given the date and the destination and outputs statistics based on the aircraft ton

Uses and Applications of Climate Forecasts for Power Utilities.

NASA Astrophysics Data System (ADS)

Changnon, Stanley A.; Changnon, Joyce M.; Changnon, David

1995-05-01

The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-depth structured interviews were conducted with 56 decision makers in six utilities to assess existing and potential uses of climate forecasts. Only 3 of the 56 use forecasts. Eighty percent of those sampled envisioned applications of climate forecasts, given certain changes and additional information. Primary applications exist in power trading, load forecasting, fuel acquisition, and systems planning, with slight differences in interests between utilities. Utility staff understand probability-based forecasts but desire climatological information related to forecasted outcomes, including analogs similar to the forecasts, and explanations of the forecasts. Desired lead times vary from a week to three months, along with forecasts of up to four seasons ahead. The new NOAA forecasts initiated in 1995 provide the lead times and longer-term forecasts desired. Major hindrances to use of forecasts are hard-to-understand formats, lack of corporate acceptance, and lack of access to expertise. Recent changes in government regulations altered the utility industry, leading to a more competitive world wherein information about future weather conditions assumes much more value. Outreach efforts by government forecast agencies appear valuable to help achieve the appropriate and enhanced use of climate forecasts by the utility industry. An opportunity for service exists also for the private weather sector.

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

NASA Astrophysics Data System (ADS)

Singh, Navneet K.; Singh, Asheesh K.; Tripathy, Manoj

2012-05-01

For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning etc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.

Price elasticity matrix of demand in power system considering demand response programs

NASA Astrophysics Data System (ADS)

Qu, Xinyao; Hui, Hongxun; Yang, Shengchun; Li, Yaping; Ding, Yi

2018-02-01

The increasing renewable energy power generations have brought more intermittency and volatility to the electric power system. Demand-side resources can improve the consumption of renewable energy by demand response (DR), which becomes one of the important means to improve the reliability of power system. In price-based DR, the sensitivity analysis of customer’s power demand to the changing electricity prices is pivotal for setting reasonable prices and forecasting loads of power system. This paper studies the price elasticity matrix of demand (PEMD). An improved PEMD model is proposed based on elasticity effect weight, which can unify the rigid loads and flexible loads. Moreover, the structure of PEMD, which is decided by price policies and load types, and the calculation method of PEMD are also proposed. Several cases are studied to prove the effectiveness of this method.

Real-time anomaly detection for very short-term load forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Jian; Hong, Tao; Yue, Meng

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Real-time anomaly detection for very short-term load forecasting

DOE PAGES

Luo, Jian; Hong, Tao; Yue, Meng

2018-01-06

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Planning a Target Renewable Portfolio using Atmospheric Modeling and Stochastic Optimization

NASA Astrophysics Data System (ADS)

Hart, E.; Jacobson, M. Z.

2009-12-01

A number of organizations have suggested that an 80% reduction in carbon emissions by 2050 is a necessary step to mitigate climate change and that decarbonization of the electricity sector is a crucial component of any strategy to meet this target. Integration of large renewable and intermittent generators poses many new problems in power system planning. In this study, we attempt to determine an optimal portfolio of renewable resources to meet best the fluctuating California load while also meeting an 80% carbon emissions reduction requirement. A stochastic optimization scheme is proposed that is based on a simplified model of the California electricity grid. In this single-busbar power system model, the load is met with generation from wind, solar thermal, photovoltaic, hydroelectric, geothermal, and natural gas plants. Wind speeds and insolation are calculated using GATOR-GCMOM, a global-through-urban climate-weather-air pollution model. Fields were produced for California and Nevada at 21km SN by 14 km WE spatial resolution every 15 minutes for the year 2006. Load data for 2006 were obtained from the California ISO OASIS database. Maximum installed capacities for wind and solar thermal generation were determined using a GIS analysis of potential development sites throughout the state. The stochastic optimization scheme requires that power balance be achieved in a number of meteorological and load scenarios that deviate from the forecasted (or modeled) data. By adjusting the error distributions of the forecasts, the model describes how improvements in wind speed and insolation forecasting may affect the optimal renewable portfolio. Using a simple model, we describe the diversity, size, and sensitivities of a renewable portfolio that is best suited to the resources and needs of California and that contributes significantly to reduction of the state’s carbon emissions.

The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

NASA Astrophysics Data System (ADS)

Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin

2017-08-01

The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

Integrating Solar PV in Utility System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mills, A.; Botterud, A.; Wu, J.

2013-10-31

This study develops a systematic framework for estimating the increase in operating costs due to uncertainty and variability in renewable resources, uses the framework to quantify the integration costs associated with sub-hourly solar power variability and uncertainty, and shows how changes in system operations may affect these costs. Toward this end, we present a statistical method for estimating the required balancing reserves to maintain system reliability along with a model for commitment and dispatch of the portfolio of thermal and renewable resources at different stages of system operations. We estimate the costs of sub-hourly solar variability, short-term forecast errors, andmore » day-ahead (DA) forecast errors as the difference in production costs between a case with “realistic” PV (i.e., subhourly solar variability and uncertainty are fully included in the modeling) and a case with “well behaved” PV (i.e., PV is assumed to have no sub-hourly variability and can be perfectly forecasted). In addition, we highlight current practices that allow utilities to compensate for the issues encountered at the sub-hourly time frame with increased levels of PV penetration. In this analysis we use the analytical framework to simulate utility operations with increasing deployment of PV in a case study of Arizona Public Service Company (APS), a utility in the southwestern United States. In our analysis, we focus on three processes that are important in understanding the management of PV variability and uncertainty in power system operations. First, we represent the decisions made the day before the operating day through a DA commitment model that relies on imperfect DA forecasts of load and wind as well as PV generation. Second, we represent the decisions made by schedulers in the operating day through hour-ahead (HA) scheduling. Peaking units can be committed or decommitted in the HA schedules and online units can be redispatched using forecasts that are improved relative to DA forecasts, but still imperfect. Finally, we represent decisions within the operating hour by schedulers and transmission system operators as real-time (RT) balancing. We simulate the DA and HA scheduling processes with a detailed unit-commitment (UC) and economic dispatch (ED) optimization model. This model creates a least-cost dispatch and commitment plan for the conventional generating units using forecasts and reserve requirements as inputs. We consider only the generation units and load of the utility in this analysis; we do not consider opportunities to trade power with neighboring utilities. We also do not consider provision of reserves from renewables or from demand-side options. We estimate dynamic reserve requirements in order to meet reliability requirements in the RT operations, considering the uncertainty and variability in load, solar PV, and wind resources. Balancing reserve requirements are based on the 2.5th and 97.5th percentile of 1-min deviations from the HA schedule in a previous year. We then simulate RT deployment of balancing reserves using a separate minute-by-minute simulation of deviations from the HA schedules in the operating year. In the simulations we assume that balancing reserves can be fully deployed in 10 min. The minute-by-minute deviations account for HA forecasting errors and the actual variability of the load, wind, and solar generation. Using these minute-by-minute deviations and deployment of balancing reserves, we evaluate the impact of PV on system reliability through the calculation of the standard reliability metric called Control Performance Standard 2 (CPS2). Broadly speaking, the CPS2 score measures the percentage of 10-min periods in which a balancing area is able to balance supply and demand within a specific threshold. Compliance with the North American Electric Reliability Corporation (NERC) reliability standards requires that the CPS2 score must exceed 90% (i.e., the balancing area must maintain adequate balance for 90% of the 10-min periods). The combination of representing DA forecast errors in the DA commitments, using 1-min PV data to simulate RT balancing, and estimates of reliability performance through the CPS2 metric, all factors that are important to operating systems with increasing amounts of PV, makes this study unique in its scope.« less

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

PubMed

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs

PubMed Central

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042

2014 Gulf of Mexico Hypoxia Forecast

USGS Publications Warehouse

Scavia, Donald; Evans, Mary Anne; Obenour, Dan

2014-01-01

The Gulf of Mexico annual summer hypoxia forecasts are based on average May total nitrogen loads from the Mississippi River basin for that year. The load estimate, recently released by USGS, is 4,761 metric tons per day. Based on that estimate, we predict the area of this summer’s hypoxic zone to be 14,000 square kilometers (95% credible interval, 8,000 to 20,000) – an “average year”. Our forecast hypoxic volume is 50 km3 (95% credible interval, 20 to 77).

Quantitative impact of aerosols on numerical weather prediction. Part I: Direct radiative forcing

NASA Astrophysics Data System (ADS)

Marquis, J. W.; Zhang, J.; Reid, J. S.; Benedetti, A.; Christensen, M.

2017-12-01

While the effects of aerosols on climate have been extensively studied over the past two decades, the impacts of aerosols on operational weather forecasts have not been carefully quantified. Despite this lack of quantification, aerosol plumes can impact weather forecasts directly by reducing surface reaching solar radiation and indirectly through affecting remotely sensed data that are used for weather forecasts. In part I of this study, the direct impact of smoke aerosol plumes on surface temperature forecasts are quantified using a smoke aerosol event affecting the United States Upper-Midwest in 2015. NCEP, ECMWF and UKMO model forecast surface temperature uncertainties are studied with respect to aerosol loading. Smoke aerosol direct cooling efficiencies are derived and the potential of including aerosol particles in operational forecasts is discussed, with the consideration of aerosol trends, especially over regions with heavy aerosol loading.

Modeling spot markets for electricity and pricing electricity derivatives

NASA Astrophysics Data System (ADS)

Ning, Yumei

Spot prices for electricity have been very volatile with dramatic price spikes occurring in restructured market. The task of forecasting electricity prices and managing price risk presents a new challenge for market players. The objectives of this dissertation are: (1) to develop a stochastic model of price behavior and predict price spikes; (2) to examine the effect of weather forecasts on forecasted prices; (3) to price electricity options and value generation capacity. The volatile behavior of prices can be represented by a stochastic regime-switching model. In the model, the means of the high-price and low-price regimes and the probabilities of switching from one regime to the other are specified as functions of daily peak load. The probability of switching to the high-price regime is positively related to load, but is still not high enough at the highest loads to predict price spikes accurately. An application of this model shows how the structure of the Pennsylvania-New Jersey-Maryland market changed when market-based offers were allowed, resulting in higher price spikes. An ARIMA model including temperature, seasonal, and weekly effects is estimated to forecast daily peak load. Forecasts of load under different assumptions about weather patterns are used to predict changes of price behavior given the regime-switching model of prices. Results show that the range of temperature forecasts from a normal summer to an extremely warm summer cause relatively small increases in temperature (+1.5%) and load (+3.0%). In contrast, the increases in prices are large (+20%). The conclusion is that the seasonal outlook forecasts provided by NOAA are potentially valuable for predicting prices in electricity markets. The traditional option models, based on Geometric Brownian Motion are not appropriate for electricity prices. An option model using the regime-switching framework is developed to value a European call option. The model includes volatility risk and allows changes in prices and volatility to be correlated. The results show that the value of a power plant is much higher using the financial option model than using traditional discounted cash flow.

The effect of real-time pricing on load shifting in a highly renewable power system dominated by generation from the renewable sources of wind and photovoltaics

NASA Astrophysics Data System (ADS)

Kies, Alexander; Brown, Tom; Schlachtberger, David; Schramm, Stefan

2017-04-01

The supply-demand imbalance is a major concern in the presence of large shares of highly variable renewable generation from sources like wind and photovoltaics (PV) in power systems. Other than the measures on the generation side, such as flexible backup generation or energy storage, sector coupling or demand side management are the most likely option to counter imbalances, therefore to ease the integration of renewable generation. Demand side management usually refers to load shifting, which comprises the reaction of electricity consumers to price fluctuations. In this work, we derive a novel methodology to model the interplay of load shifting and provided incentives via real-time pricing in highly renewable power systems. We use weather data to simulate generation from the renewable sources of wind and photovoltaics, as well as historical load data, split into different consumption categories, such as, heating, cooling, domestic, etc., to model a simplified power system. Together with renewable power forecast data, a simple market model and approaches to incorporate sector coupling [1] and load shifting [2,3], we model the interplay of incentives and load shifting for different scenarios (e.g., in dependency of the risk-aversion of consumers or the forecast horizon) and demonstrate the practical benefits of load shifting. First, we introduce the novel methodology and compare it with existing approaches. Secondly, we show results of numerical simulations on the effects of load shifting: It supports the integration of PV power by providing a storage, which characteristics can be described as "daily" and provides a significant amount of balancing potential. Lastly, we propose an experimental setup to obtain empirical data on end-consumer load-shifting behaviour in response to price incentives. References [1] Brown, T., Schlachtberger, D., Kies. A., Greiner, M., Sector coupling in a highly renewable European energy system, Proc. of the 15th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants, Vienna, Austria, 15.-17. November 2016 [2] Kleinhans, D.: Towards a systematic characterization of the potential of demand side management, arXiv preprint arXiv:1401.4121, 2014 [3] Kies, A., Schyska, B. U., von Bremen, L., The Demand Side Management Potential to Balance a Highly Renewable European Power System. Energies, 9(11), 955, 2016

Research on Operation Strategy for Bundled Wind-thermal Generation Power Systems Based on Two-Stage Optimization Model

NASA Astrophysics Data System (ADS)

Sun, Congcong; Wang, Zhijie; Liu, Sanming; Jiang, Xiuchen; Sheng, Gehao; Liu, Tianyu

2017-05-01

Wind power has the advantages of being clean and non-polluting and the development of bundled wind-thermal generation power systems (BWTGSs) is one of the important means to improve wind power accommodation rate and implement “clean alternative” on generation side. A two-stage optimization strategy for BWTGSs considering wind speed forecasting results and load characteristics is proposed. By taking short-term wind speed forecasting results of generation side and load characteristics of demand side into account, a two-stage optimization model for BWTGSs is formulated. By using the environmental benefit index of BWTGSs as the objective function, supply-demand balance and generator operation as the constraints, the first-stage optimization model is developed with the chance-constrained programming theory. By using the operation cost for BWTGSs as the objective function, the second-stage optimization model is developed with the greedy algorithm. The improved PSO algorithm is employed to solve the model and numerical test verifies the effectiveness of the proposed strategy.

Evaluating the Impacts of Real-Time Pricing on the Cost and Value of Wind Generation

DOE PAGES

Siohansi, Ramteen

2010-05-01

One of the costs associated with integrating wind generation into a power system is the cost of redispatching the system in real-time due to day-ahead wind resource forecast errors. One possible way of reducing these redispatch costs is to introduce demand response in the form of real-time pricing (RTP), which could allow electricity demand to respond to actual real-time wind resource availability using price signals. A day-ahead unit commitment model with day-ahead wind forecasts and a real-time dispatch model with actual wind resource availability is used to estimate system operations in a high wind penetration scenario. System operations are comparedmore » to a perfect foresight benchmark, in which actual wind resource availability is known day-ahead. The results show that wind integration costs with fixed demands can be high, both due to real-time redispatch costs and lost load. It is demonstrated that introducing RTP can reduce redispatch costs and eliminate loss of load events. Finally, social surplus with wind generation and RTP is compared to a system with neither and the results demonstrate that introducing wind and RTP into a market can result in superadditive surplus gains.« less

Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

NASA Astrophysics Data System (ADS)

Sun, Xinyao; Wang, Xue; Wu, Jiangwei; Liu, Youda

2014-05-01

Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufacturing center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Short-term forecasts gain in accuracy. [Regression technique using ''Box-Jenkins'' analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

Box-Jenkins time-series models offer accuracy for short-term forecasts that compare with large-scale macroeconomic forecasts. Utilities need to be able to forecast peak demand in order to plan their generating, transmitting, and distribution systems. This new method differs from conventional models by not assuming specific data patterns, but by fitting available data into a tentative pattern on the basis of auto-correlations. Three types of models (autoregressive, moving average, or mixed autoregressive/moving average) can be used according to which provides the most appropriate combination of autocorrelations and related derivatives. Major steps in choosing a model are identifying potential models, estimating the parametersmore » of the problem, and running a diagnostic check to see if the model fits the parameters. The Box-Jenkins technique is well suited for seasonal patterns, which makes it possible to have as short as hourly forecasts of load demand. With accuracy up to two years, the method will allow electricity price-elasticity forecasting that can be applied to facility planning and rate design. (DCK)« less

Analysis of rock mass dynamic impact influence on the operation of a powered roof support control system

NASA Astrophysics Data System (ADS)

Szurgacz, Dawid; Brodny, Jaroław

2018-01-01

A powered roof support is a machine responsible for protection of an underground excavation against deformation generated by rock mass. In the case of dynamic impact of rock mass, the proper level of protection is hard to achieve. Therefore, the units of the roof support and its components are subject to detailed tests aimed at acquiring greater reliability, efficiency and efficacy. In the course of such test, however, it is not always possible to foresee values of load that may occur in actual conditions. The article presents a case of a dynamic load impacting the powered roof support during a high-energy tremor in an underground hard coal mine. The authors discuss the method for selecting powered roof support units proper for specific forecasted load conditions. The method takes into account the construction of the support and mining and geological conditions of an excavation. Moreover, the paper includes tests carried out on hydraulic legs and yield valves which were responsible for additional yielding of the support. Real loads impacting the support unit during tremors are analysed. The results indicated that the real registered values of the load were significantly greater than the forecasted values. The analysis results of roof support operation during dynamic impact generated by the rock mass (real life conditions) prompted the authors to develop a set of recommendations for manufacturers and users of powered roof supports. These include, inter alia, the need for innovative solutions for testing hydraulic section systems.

Advanced Cloud Forecasting for Solar Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; Parker, M. J.

2017-12-01

A power utility must decide days in advance how it will allocate projected loads among its various generating sources. If the latter includes solar plants, the utility must predict how much energy the plants will produce - any shortfall will have to be compensated for by purchasing power as it is needed, when it is more expensive. To avoid this, utilities often err on the side of caution and assume that a relatively small amount of solar energy will be available, and allocate correspondingly more load to coal-fired plants. If solar irradiance can be predicted more accurately, utilities can be more confident that the predicted solar energy will indeed be available when needed, and assign solar plants a larger share of the future load. Solar power production is increasing in the Southeast, but is often hampered by irregular cloud fields, especially during high-pressure periods when rapid afternoon thunderstorm development can occur during what was predicted to be a clear day. We are currently developing an analog forecasting system to predict solar irradiance at the surface at the Savannah River Site in South Carolina, with the goal of improving predictions of available solar energy. Analog forecasting is based on the assumption that similar initial conditions will lead to similar outcomes, and involves the use of an algorithm to look through the weather patterns of the past to identify previous conditions (the analogs) similar to those of today. For our application, we select three predictor variables - sea-level pressure, 700mb geopotential, and 700mb humidity. These fields for the current day are compared to those from past days, and a weighted combination of the differences (defined by a cost function) is used to select the five best analog days. The observed solar irradiance values subsequent to the dates of those analogs are then combined to represent the forecast for the next day. We will explain how we apply the analog process, and compare it to existing solar forecasts.

76 FR 66229 - Transmission Planning Reliability Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-26

... Transmission Services, at all demand levels over the range of forecast system demands, under the contingency... any planned firm load that is not directly served by the elements that are removed from service as a... to plan for the loss of firm service for a single contingency, the Commission finds that their...

An early warning system for groundwater pollution based on the assessment of groundwater pollution risks.

NASA Astrophysics Data System (ADS)

Zhang, Weihong.; Zhao, Yongsheng; Hong, Mei; Guo, Xiaodong

2009-04-01

Groundwater pollution usually is complex and concealed, remediation of which is difficult, high cost, time-consuming, and ineffective. An early warning system for groundwater pollution is needed that detects groundwater quality problems and gets the information necessary to make sound decisions before massive groundwater quality degradation occurs. Groundwater pollution early warning were performed by considering comprehensively the current groundwater quality, groundwater quality varying trend and groundwater pollution risk . The map of the basic quality of the groundwater was obtained by fuzzy comprehensive evaluation or BP neural network evaluation. Based on multi-annual groundwater monitoring datasets, Water quality state in sometime of the future was forecasted using time-sequenced analyzing methods. Water quality varying trend was analyzed by Spearman's rank correlative coefficient.The relative risk map of groundwater pollution was estimated through a procedure that identifies, cell by cell,the values of three factors, that is inherent vulnerability, load risk of pollution source and contamination hazard. DRASTIC method was used to assess inherent vulnerability of aquifer. Load risk of pollution source was analyzed based on the potential of contamination and pollution degree. Assessment index of load risk of pollution source which involves the variety of pollution source, quantity of contaminants, releasing potential of pollutants, and distance were determined. The load risks of all sources considered by GIS overlay technology. Early warning model of groundwater pollution combined with ComGIS technology organically, the regional groundwater pollution early-warning information system was developed, and applied it into Qiqiha'er groundwater early warning. It can be used to evaluate current water quality, to forecast water quality changing trend, and to analyze space-time influencing range of groundwater quality by natural process and human activities. Keywords: groundwater pollution, early warning, aquifer vulnerability, pollution load, pollution risk, ComGIS

2013 Gulf of Mexico Hypoxia Forecast

USGS Publications Warehouse

Scavia, Donald; Evans, Mary Anne; Obenour, Dan

2013-01-01

The Gulf of Mexico annual summer hypoxia forecasts are based on average May total nitrogen loads from the Mississippi River basin for that year. The load estimate, recently released by USGS, is 7,316 metric tons per day. Based on that estimate, we predict the area of this summer’s hypoxic zone to be 18,900 square kilometers (95% credible interval, 13,400 to 24,200), the 7th largest reported and about the size of New Jersey. Our forecast hypoxic volume is 74.5 km3 (95% credible interval, 51.5 to 97.0), also the 7th largest on record.

The promise of air cargo-system aspects and vehicle design

NASA Technical Reports Server (NTRS)

Whitehead, A. H., Jr.

1977-01-01

A review of the current operation of the air cargo system is presented and the prospects for the future are discussed. Attention is given to air cargo demand forecasts, the economics of air cargo transport, the development of an integrated air cargo system, and the evolution of airfreighter design. Particular emphasis is placed on the span-distributed load concept, examining the Boeing, Douglas, and Lockheed spanloaders.

7 CFR 1710.204 - Filing requirements for borrowers that must maintain an approved load forecast on an ongoing basis.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Filing requirements for borrowers that must maintain an approved load forecast on an ongoing basis. 1710.204 Section 1710.204 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO...

Analysis of temperature changes on three-phase synchronous generator using infrared: comparison between balanced and unbalanced load

NASA Astrophysics Data System (ADS)

Amien, S.; Yoga, W.; Fahmi, F.

2018-02-01

Synchronous generators are a major tool in an electrical energy generating systems, the load supplied by the generator is unbalanced. This paper discusses the effect of synchronous generator temperature on the condition of balanced load and unbalanced load, which will then be compared with the measurement result of both states of the generator. Unbalanced loads can be caused by various asymmetric disturbances in the power system and the failure of load forecasting studies so that the load distribution in each phase is not the same and causing the excessive heat of the generator. The method used in data collection was by using an infrared thermometer and resistance calculation method. The temperature comparison result between the resistive, inductive and capacitive loads in the highest temperature balance occured when the generator is loaded with a resistive load, where T = 31.9 ° C and t = 65 minutes. While in a state of unbalanced load the highest temperature occured when the generator is loaded with a capacitive load, where T = 40.1 ° C and t = 60 minutes. By understanding this behavior, we can maintain the generator for longer operation life.

Network-Cognizant Voltage Droop Control for Distribution Grids

DOE PAGES

Baker, Kyri; Bernstein, Andrey; Dall'Anese, Emiliano; ...

2017-08-07

Our paper examines distribution systems with a high integration of distributed energy resources (DERs) and addresses the design of local control methods for real-time voltage regulation. Particularly, the paper focuses on proportional control strategies where the active and reactive output-powers of DERs are adjusted in response to (and proportionally to) local changes in voltage levels. The design of the voltage-active power and voltage-reactive power characteristics leverages suitable linear approximation of the AC power-flow equations and is network-cognizant; that is, the coefficients of the controllers embed information on the location of the DERs and forecasted non-controllable loads/injections and, consequently, on themore » effect of DER power adjustments on the overall voltage profile. We pursued a robust approach to cope with uncertainty in the forecasted non-controllable loads/power injections. Stability of the proposed local controllers is analytically assessed and numerically corroborated.« less

Network-Cognizant Voltage Droop Control for Distribution Grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri; Bernstein, Andrey; Dall'Anese, Emiliano

Our paper examines distribution systems with a high integration of distributed energy resources (DERs) and addresses the design of local control methods for real-time voltage regulation. Particularly, the paper focuses on proportional control strategies where the active and reactive output-powers of DERs are adjusted in response to (and proportionally to) local changes in voltage levels. The design of the voltage-active power and voltage-reactive power characteristics leverages suitable linear approximation of the AC power-flow equations and is network-cognizant; that is, the coefficients of the controllers embed information on the location of the DERs and forecasted non-controllable loads/injections and, consequently, on themore » effect of DER power adjustments on the overall voltage profile. We pursued a robust approach to cope with uncertainty in the forecasted non-controllable loads/power injections. Stability of the proposed local controllers is analytically assessed and numerically corroborated.« less

Systematic Evaluation of Stochastic Methods in Power System Scheduling and Dispatch with Renewable Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yishen; Zhou, Zhi; Liu, Cong

2016-08-01

As more wind power and other renewable resources are being integrated into the electric power grid, the forecast uncertainty brings operational challenges for the power system operators. In this report, different operational strategies for uncertainty management are presented and evaluated. A comprehensive and consistent simulation framework is developed to analyze the performance of different reserve policies and scheduling techniques under uncertainty in wind power. Numerical simulations are conducted on a modified version of the IEEE 118-bus system with a 20% wind penetration level, comparing deterministic, interval, and stochastic unit commitment strategies. The results show that stochastic unit commitment provides amore » reliable schedule without large increases in operational costs. Moreover, decomposition techniques, such as load shift factor and Benders decomposition, can help in overcoming the computational obstacles to stochastic unit commitment and enable the use of a larger scenario set to represent forecast uncertainty. In contrast, deterministic and interval unit commitment tend to give higher system costs as more reserves are being scheduled to address forecast uncertainty. However, these approaches require a much lower computational effort Choosing a proper lower bound for the forecast uncertainty is important for balancing reliability and system operational cost in deterministic and interval unit commitment. Finally, we find that the introduction of zonal reserve requirements improves reliability, but at the expense of higher operational costs.« less

Towards Optimal Operation of the Reservoir System in Upper Yellow River: Incorporating Long- and Short-term Operations and Using Rolling Updated Hydrologic Forecast Information

NASA Astrophysics Data System (ADS)

Si, Y.; Li, X.; Li, T.; Huang, Y.; Yin, D.

2016-12-01

The cascade reservoirs in Upper Yellow River (UYR), one of the largest hydropower bases in China, play a vital role in peak load and frequency regulation for Northwest China Power Grid. The joint operation of this system has been put forward for years whereas has not come into effect due to management difficulties and inflow uncertainties, and thus there is still considerable improvement room for hydropower production. This study presents a decision support framework incorporating long- and short-term operation of the reservoir system. For long-term operation, we maximize hydropower production of the reservoir system using historical hydrological data of multiple years, and derive operating rule curves for storage reservoirs. For short-term operation, we develop a program consisting of three modules, namely hydrologic forecast module, reservoir operation module and coordination module. The coordination module is responsible for calling the hydrologic forecast module to acquire predicted inflow within a short-term horizon, and transferring the information to the reservoir operation module to generate optimal release decision. With the hydrologic forecast information updated, the rolling short-term optimization is iterated until the end of operation period, where the long-term operating curves serve as the ending storage target. As an application, the Digital Yellow River Integrated Model (referred to as "DYRIM", which is specially designed for runoff-sediment simulation in the Yellow River basin by Tsinghua University) is used in the hydrologic forecast module, and the successive linear programming (SLP) in the reservoir operation module. The application in the reservoir system of UYR demonstrates that the framework can effectively support real-time decision making, and ensure both computational accuracy and speed. Furthermore, it is worth noting that the general framework can be extended to any other reservoir system with any or combination of hydrological model(s) to forecast and any solver to optimize the operation of reservoir system.

Building Technology Forecast and Evaluation (BTFE). Volume 2. Evaluation of Two Structural Systems

DTIC Science & Technology

1990-11-01

insulative foam ( expanded polystyrene ) strips between each truss. The assembly is held together with 14-gauge wires welded to the trusses on 2-in. centers...structural load bearing qualities expanded polystyrene . No taping and mudding. Ar. ~J~ .wplrtpd( at each irllnfrnPllo Tile I hin- set or float over

Chesapeake Bay hypoxic volume forecasts and results

USGS Publications Warehouse

Scavia, Donald; Evans, Mary Anne

2013-01-01

The 2013 Forecast - Given the average Jan-May 2013 total nitrogen load of 162,028 kg/day, this summer’s hypoxia volume forecast is 6.1 km3, slightly smaller than average size for the period of record and almost the same as 2012. The late July 2013 measured volume was 6.92 km3.

An Overview of ANN Application in the Power Industry

NASA Technical Reports Server (NTRS)

Niebur, D.

1995-01-01

The paper presents a survey on the development and experience with artificial neural net (ANN) applications for electric power systems, with emphasis on operational systems. The organization and constraints of electric utilities are reviewed, motivations for investigating ANN are identified, and a current assessment is given from the experience of 2400 projects using ANN for load forecasting, alarm processing, fault detection, component fault diagnosis, static and dynamic security analysis, system planning, and operation planning.

Power Flow Simulations of a More Renewable California Grid Utilizing Wind and Solar Insolation Forecasting

NASA Astrophysics Data System (ADS)

Hart, E. K.; Jacobson, M. Z.; Dvorak, M. J.

2008-12-01

Time series power flow analyses of the California electricity grid are performed with extensive addition of intermittent renewable power. The study focuses on the effects of replacing non-renewable and imported (out-of-state) electricity with wind and solar power on the reliability of the transmission grid. Simulations are performed for specific days chosen throughout the year to capture seasonal fluctuations in load, wind, and insolation. Wind farm expansions and new wind farms are proposed based on regional wind resources and time-dependent wind power output is calculated using a meteorological model and the power curves of specific wind turbines. Solar power is incorporated both as centralized and distributed generation. Concentrating solar thermal plants are modeled using local insolation data and the efficiencies of pre-existing plants. Distributed generation from rooftop PV systems is included using regional insolation data, efficiencies of common PV systems, and census data. The additional power output of these technologies offsets power from large natural gas plants and is balanced for the purposes of load matching largely with hydroelectric power and by curtailment when necessary. A quantitative analysis of the effects of this significant shift in the electricity portfolio of the state of California on power availability and transmission line congestion, using a transmission load-flow model, is presented. A sensitivity analysis is also performed to determine the effects of forecasting errors in wind and insolation on load-matching and transmission line congestion.

NREL Integrate: RCS-4-42326

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hudgins, Andrew P.; Waight, Jim; Grover, Shailendra

OMNETRIC Corp., Duke Energy, CPS Energy, and the University of Texas at San Antonio (UTSA) created a project team to execute the project 'OpenFMB Reference Architecture Demonstration.' The project included development and demonstration of concepts that will enable the electric utility grid to host larger penetrations of renewable resources. The project concept calls for the aggregation of renewable resources and loads into microgrids and the control of these microgrids with an implementation of the OpenFMB Reference Architecture. The production of power from the renewable resources that are appearing on the grid today is very closely linked to the weather. Themore » difficulty of forecasting the weather, which is well understood, leads to difficulty in forecasting the production of renewable resources. The current state of the art in forecasting the power production from renewables (solar PV and wind) are accuracies in the range of 12-25 percent NMAE. In contrast the demand for electricity aggregated to the system level, is easier to predict. The state of the art of demand forecasting done, 24 hours ahead, is about 2-3% MAPE. Forecasting the load to be supplied from conventional resources (demand minus generation from renewable resources) is thus very hard to forecast. This means that even a few hours before the time of consumption, there can be considerable uncertainty over what must be done to balance supply and demand. Adding to the problem of difficulty of forecasting, is the reality of the variability of the actual production of power from renewables. Due to the variability of wind speeds and solar insolation, the actual output of power from renewable resources can vary significantly over a short period of time. Gusts of winds result is variation of power output of wind turbines. The shadows of clouds moving over solar PV arrays result in the variation of power production of the array. This compounds the problem of balancing supply and demand in real time. Establishing a control system that can manage distribution systems with large penetrations of renewable resources is difficult due to two major issues: (1) the lack of standardization and interoperability between the vast array of equipment in operation and on the market, most of which use different and proprietary means of communication and (2) the magnitude of the network and the information it generates and consumes. The objective of this project is to provide the industry with a design concept and tools that will enable the electric power grid to overcome these barriers and support a larger penetration of clean energy from renewable resources.« less

STS-114: Discovery L-2 Countdown Status Briefing

NASA Technical Reports Server (NTRS)

2005-01-01

George Diller of NASA Public Affairs hosted this briefing. Pete Nickolenko, NASA Test Director; Scott Higgenbotham, STS-114 Payload-Mission Manager; Cathy Winters, Shuttle Weather Officer were present. Pete reports his team has completed the avionics system check ups, servicing of the cryogenic tanks will take about seven hours that day, and will perform engine system checks and pad close outs come evening. Pete also summarized other standard close out activities: check ups of the Orbiter and ground communications network, rotary service, structure retraction, and external tank load (ETL). Pete reported that the mission will be 12 days with two weather contingency days, and end of mission landing scheduled at Kennedy Space Center (KSC) at approximately 11:00 in the morning, Eastern time on July 25th. Scott briefly reported that all hardware is on board Discovery, closed out, and ready to fly. Cathy reported that hurricane Dennis moved to the North and looking forward to launch. She mentioned of a new hurricane looming and will be named Emily, spotted some crosswinds which will migrate to the west, there is 30% probability weather prohibiting launch. Cathy further gave current weather forecast supported with charts: the Launch Forecast, Tanking Forecast, SRB (Shuttle Solid Rocket Booster) Forecast, CONUS and TAL Launch Sites Forecast, and with 24 hours and 48 hours turn around plan. Launch constraints, weather, crosswinds, cloud cover, ground imagery system, launch countdown, launch crews, mission management simulations, launch team simulations were topics covered with the News Media.

Dynamic linear models to explore time-varying suspended sediment-discharge rating curves

NASA Astrophysics Data System (ADS)

Ahn, Kuk-Hyun; Yellen, Brian; Steinschneider, Scott

2017-06-01

This study presents a new method to examine long-term dynamics in sediment yield using time-varying sediment-discharge rating curves. Dynamic linear models (DLMs) are introduced as a time series filter that can assess how the relationship between streamflow and sediment concentration or load changes over time in response to a wide variety of natural and anthropogenic watershed disturbances or long-term changes. The filter operates by updating parameter values using a recursive Bayesian design that responds to 1 day-ahead forecast errors while also accounting for observational noise. The estimated time series of rating curve parameters can then be used to diagnose multiscale (daily-decadal) variability in sediment yield after accounting for fluctuations in streamflow. The technique is applied in a case study examining changes in turbidity load, a proxy for sediment load, in the Esopus Creek watershed, part of the New York City drinking water supply system. The results show that turbidity load exhibits a complex array of variability across time scales. The DLM highlights flood event-driven positive hysteresis, where turbidity load remained elevated for months after large flood events, as a major component of dynamic behavior in the rating curve relationship. The DLM also produces more accurate 1 day-ahead loading forecasts compared to other static and time-varying rating curve methods. The results suggest that DLMs provide a useful tool for diagnosing changes in sediment-discharge relationships over time and may help identify variability in sediment concentrations and loads that can be used to inform dynamic water quality management.

Final Technical Report for Contract No. DE-EE0006332, "Integrated Simulation Development and Decision Support Tool-Set for Utility Market and Distributed Solar Power Generation"

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cormier, Dallas; Edra, Sherwin; Espinoza, Michael

This project will enable utilities to develop long-term strategic plans that integrate high levels of renewable energy generation, and to better plan power system operations under high renewable penetration. The program developed forecast data streams for decision support and effective integration of centralized and distributed solar power generation in utility operations. This toolset focused on real time simulation of distributed power generation within utility grids with the emphasis on potential applications in day ahead (market) and real time (reliability) utility operations. The project team developed and demonstrated methodologies for quantifying the impact of distributed solar generation on core utility operations,more » identified protocols for internal data communication requirements, and worked with utility personnel to adapt the new distributed generation (DG) forecasts seamlessly within existing Load and Generation procedures through a sophisticated DMS. This project supported the objectives of the SunShot Initiative and SUNRISE by enabling core utility operations to enhance their simulation capability to analyze and prepare for the impacts of high penetrations of solar on the power grid. The impact of high penetration solar PV on utility operations is not only limited to control centers, but across many core operations. Benefits of an enhanced DMS using state-of-the-art solar forecast data were demonstrated within this project and have had an immediate direct operational cost savings for Energy Marketing for Day Ahead generation commitments, Real Time Operations, Load Forecasting (at an aggregate system level for Day Ahead), Demand Response, Long term Planning (asset management), Distribution Operations, and core ancillary services as required for balancing and reliability. This provided power system operators with the necessary tools and processes to operate the grid in a reliable manner under high renewable penetration.« less

Power control and management of the grid containing largescale wind power systems

NASA Astrophysics Data System (ADS)

Aula, Fadhil Toufick

The ever increasing demand for electricity has driven many countries toward the installation of new generation facilities. However, concerns such as environmental pollution and global warming issues, clean energy sources, high costs associated with installation of new conventional power plants, and fossil fuels depletion have created many interests in finding alternatives to conventional fossil fuels for generating electricity. Wind energy is one of the most rapidly growing renewable power sources and wind power generations have been increasingly demanded as an alternative to the conventional fossil fuels. However, wind power fluctuates due to variation of wind speed. Therefore, large-scale integration of wind energy conversion systems is a threat to the stability and reliability of utility grids containing these systems. They disturb the balance between power generation and consumption, affect the quality of the electricity, and complicate load sharing and load distribution managing and planning. Overall, wind power systems do not help in providing any services such as operating and regulating reserves to the power grid. In order to resolve these issues, research has been conducted in utilizing weather forecasting data to improve the performance of the wind power system, reduce the influence of the fluctuations, and plan power management of the grid containing large-scale wind power systems which consist of doubly-fed induction generator based energy conversion system. The aims of this research, my dissertation, are to provide new methods for: smoothing the output power of the wind power systems and reducing the influence of their fluctuations, power managing and planning of a grid containing these systems and other conventional power plants, and providing a new structure of implementing of latest microprocessor technology for controlling and managing the operation of the wind power system. In this research, in order to reduce and smooth the fluctuations, two methods are presented. The first method is based on a de-loaded technique while the other method is based on utilizing multiple storage facilities. The de-loaded technique is based on characteristics of the power of a wind turbine and estimation of the generated power according to weather forecasting data. The technique provides a reference power by which the wind power system will operate and generate a smooth power. In contrast, utilizing storage facilities will allow the wind power system to operate at its maximum tracking power points' strategy. Two types of energy storages are considered in this research, battery energy storage system (BESS) and pumped-hydropower storage system (PHSS), to suppress the output fluctuations and to support the wind power system to follow the system load demands. Furthermore, this method provides the ability to store energy when there is a surplus of the generated power and to reuse it when there is a shortage of power generation from wind power systems. Both methods are new in terms of utilizing of the techniques and wind speed data. A microprocessor embedded system using an IntelRTM Atom(TM) processor is presented for controlling the wind power system and for providing the remote communication for enhancing the operation of the individual wind power system in a wind farm. The embedded system helps the wind power system to respond and to follow the commands of the central control of the power system. Moreover, it enhances the performance of the wind power system through self-managing, self-functioning, and self-correcting. Finally, a method of system power management and planning is modeled and studied for a grid containing large-scale wind power systems. The method is based on a new technique through constructing a new load demand curve (NLDC) from merging the estimation of generated power from wind power systems and forecasting of the load. To summarize, the methods and their results presented in this dissertation, enhance the operation of the large-scale wind power systems and reduce their drawbacks on the operation of the power grid.

7 CFR 1710.302 - Financial forecasts-power supply borrowers.

Code of Federal Regulations, 2013 CFR

2013-01-01

... facilities; (3) Provide an in-depth analysis of the regional markets for power if loan feasibility depends to any degree on a borrower's ability to sell surplus power while its system loads grow to meet the... sensitivity analysis if required by RUS pursuant to § 1710.300(d)(5). (e) The projections shall be coordinated...

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

NASA Astrophysics Data System (ADS)

Zhang, Li

With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman filter (EKF) can be used as an integrated adaptive learning and confidence interval estimation algorithm for neural networks, with fast convergence and small confidence intervals. However, EKF learning is computationally expensive because it involves high dimensional matrix manipulations. A modified U-D factorization within the decoupled EKF (DEKF-UD) framework is developed in this research. The computational efficiency and numerical stability are significantly improved.

Visualizing and Integrating AFSCN Utilization into a Common Operational Picture

NASA Astrophysics Data System (ADS)

Hays, B.; Carlile, A.; Mitchell, T.

The Department of Defense (DoD) and the 50th Space Network Operations Group Studies and Analysis branch (50th SCS/SCXI), located at Schriever AFB Colorado, face the unique challenge of forecasting the expected near term and future utilization of the Air Force Satellite Control Network (AFSCN). The forecasting timeframe covers the planned load from the current date to ten years out. The various satellite missions, satellite requirements, orbital regions, and ground architecture dynamics provide the model inputs and constraints that are used in generating the forecasted load. The AFSCN is the largest network the Air Force uses to control satellites worldwide. Each day, network personnel perform over 500 scheduled events-from satellite maneuvers to critical data downloads. The Forecasting Objective is to provide leadership with the insights necessary to manage the network today and tomorrow. For both today's needs and future needs, SCXI develops AFSCN utilization forecasts to optimize the ground system's coverage and capacity to meet user satellite requirements. SCXI also performs satellite program specific studies to determine network support feasibility. STK and STK Scheduler form the core of the tools used by SCXI. To establish this tool suite, we had to evaluate, evolve, and validate both the COTS products and our own developed code and processes. This began with calibrating the network model to emulate the real life scheduling environment of the AFSCN. Multiple STK Scheduler optimizing (de-confliction) algorithms, including Multi-Pass, Sequential, Random, and Neural, were evaluated and adjusted to determine applicability to the model and the accuracy of the prediction. Additionally, the scheduling Figure of Merit (FOM), which permits custom weighting of various parameters, was analyzed and tested to achieve the most accurate real life result. With the inherent capabilities of STK and the ability to wrap and automate output, SCXI is now able to visually communicate satellite loads in a manner never seen before in AFSCN management meetings. Scenarios such as regional antenna load stress, satellite missed opportunities, and the overall network "big picture" can be visually displayed in 3D versus the textual and line graph methods used for many years. This is the first step towards an integrated space awareness picture with an operational focus. SCXI is working on taking the visual forecast concept farther and begin fusing multiple sources of data to build a 50 SW Common Operating Picture (COP). The vision is to integrate more effective orbital determination processes, resource outages, current and forecasted satellite mission requirements, and future architectural changes into a real-time visual status to enable quick and responsive decisions. This COP would be utilized in a Wing Operations Center to provide up to the minute network status on where satellites are, which ground resources are in contact with them, and what resources are down. The ability to quickly absorb and process this data will enhance decision analysis and save valuable time in both day to day operations and wartime scenarios.

Feasibility study for the Swaziland/Mozambique interconnector. Final report. Export trade information

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-11-01

This study, conducted by Black & Veatch, was funded by the U.S. Trade and Development Agency. The report, produced for the Ministry of National Resources, Energy and Environment (MNRE) of Swaziland, determines the least cost capacity expansion option to meet the future power demand and system reliability criteria of Swaziland, with particular emphasis on the proposed interconnector between Swaziland and Mozambique. Volume 2, the Final Report, contains the following sections: (1.0) Introduction; (2.0) Review of SEB Power System; (3.0) SEB Load Forecast and Review; (4.0) SEB Load Forecast Revision; (5.0) The SEB Need for Power; (6.0) SEB System Development Planmore » Review; (7.0) Southern Mozambique EdM power System Review; (8.0) Southern Mozambique EdM Energy and Demand; (9.0) Supply Side Capacity Options for Swaziland and Mozambique; (10.0) SEB Expansion Plan Development; (11.0) EdM Expansion Plan Development; (12.0) Cost Sharing of the Interconnector; (13.0) Enviroinmental Evaluation of Interconnector Options; (14.0) Generation/Transmission Trade Offs; (15.0) Draft Interconnection Agreement and Contract Packages; (16.0) Transmission System Study; (17.0) Automatic General Control; (18.0) Automatic Startup and Shutdown of Hydro Electric Power Plants; (19.0) Communications and Metering; (20.0) Conclusions and Recommendations; Appendix A: Demand Side Management Primer; Appendix B. PURPA and Avoided Cost Calculations.« less

Minimization of Impact from Electric Vehicle Supply Equipment to the Electric Grid Using a Dynamically Controlled Battery Bank for Peak Load Shaving

DOE Office of Scientific and Technical Information (OSTI.GOV)

Castello, Charles C

This research presents a comparison of two control systems for peak load shaving using local solar power generation (i.e., photovoltaic array) and local energy storage (i.e., battery bank). The purpose is to minimize load demand of electric vehicle supply equipment (EVSE) on the electric grid. A static and dynamic control system is compared to decrease demand from EVSE. Static control of the battery bank is based on charging and discharging to the electric grid at fixed times. Dynamic control, with 15-minute resolution, forecasts EVSE load based on data analysis of collected data. In the proposed dynamic control system, the sigmoidmore » function is used to shave peak loads while limiting scenarios that can quickly drain the battery bank. These control systems are applied to Oak Ridge National Laboratory s (ORNL) solar-assisted electric vehicle (EV) charging stations. This installation is composed of three independently grid-tied sub-systems: (1) 25 EVSE; (2) 47 kW photovoltaic (PV) array; and (3) 60 kWh battery bank. The dynamic control system achieved the greatest peak load shaving, up to 34% on a cloudy day and 38% on a sunny day. The static control system was not ideal; peak load shaving was 14.6% on a cloudy day and 12.7% on a sunny day. Simulations based on ORNL data shows solar-assisted EV charging stations combined with the proposed dynamic battery control system can negate up to 89% of EVSE load demand on sunny days.« less

The Delicate Analysis of Short-Term Load Forecasting

NASA Astrophysics Data System (ADS)

Song, Changwei; Zheng, Yuan

2017-05-01

This paper proposes a new method for short-term load forecasting based on the similar day method, correlation coefficient and Fast Fourier Transform (FFT) to achieve the precision analysis of load variation from three aspects (typical day, correlation coefficient, spectral analysis) and three dimensions (time dimension, industry dimensions, the main factors influencing the load characteristic such as national policies, regional economic, holidays, electricity and so on). First, the branch algorithm one-class-SVM is adopted to selection the typical day. Second, correlation coefficient method is used to obtain the direction and strength of the linear relationship between two random variables, which can reflect the influence caused by the customer macro policy and the scale of production to the electricity price. Third, Fourier transform residual error correction model is proposed to reflect the nature of load extracting from the residual error. Finally, simulation result indicates the validity and engineering practicability of the proposed method.

Real-Time Analysis of a Sensor's Data for Automated Decision Making in an IoT-Based Smart Home.

PubMed

Khan, Nida Saddaf; Ghani, Sayeed; Haider, Sajjad

2018-05-25

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor's streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Ecological Forecasting: Microbial Contamination and Atmospheric Loadings of Nutrients to Land and Water

EPA Science Inventory

The development of ecological forecasts, namely, methodologies to predict the chemical, biological, and physical changes in terrestrial and aquatic ecosystems is desirable so that effective strategies for reducing the adverse impacts of human activities and extreme natural events...

Analysis of vibrational load influence upon passengers in trains with a compulsory body tilt

NASA Astrophysics Data System (ADS)

Antipin, D. Ya; Kobishchanov, V. V.; Lapshin, V. F.; Mitrakov, A. S.; Shorokhov, S. G.

2017-02-01

The procedure for forecasting the vibrational load influence upon passengers of trains of rolling stocks equipped with a system of a compulsory body tilt on railroad curves is offered. The procedure is based on the use of computer simulation methods and application of solid-state models of anthropometrical mannequins. As a result of the carried out investigations, there are substantiated criteria of the comfort level estimate for passengers in the rolling-stock under consideration. The procedure is approved by the example of the promising domestic rolling stock with a compulsory body tilt on railroad curves.

Bi-Level Arbitrage Potential Evaluation for Grid-Scale Energy Storage Considering Wind Power and LMP Smoothing Effect

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Hantao; Li, Fangxing; Fang, Xin

Our paper deals with extended-term energy storage (ES) arbitrage problems to maximize the annual revenue in deregulated power systems with high penetration wind power. The conventional ES arbitrage model takes the locational marginal prices (LMP) as an input and is unable to account for the impacts of ES operations on system LMPs. This paper proposes a bi-level ES arbitrage model, where the upper level maximizes the ES arbitrage revenue and the lower level simulates the market clearing process considering wind power and ES. The bi-level model is formulated as a mathematical program with equilibrium constraints (MPEC) and then recast intomore » a mixed-integer linear programming (MILP) using strong duality theory. Wind power fluctuations are characterized by the GARCH forecast model and the forecast error is modeled by forecast-bin based Beta distributions. Case studies are performed on a modified PJM 5-bus system and an IEEE 118-bus system with a weekly time horizon over an annual term to show the validity of the proposed bi-level model. The results from the conventional model and the bi-level model are compared under different ES power and energy ratings, and also various load and wind penetration levels.« less

Bi-Level Arbitrage Potential Evaluation for Grid-Scale Energy Storage Considering Wind Power and LMP Smoothing Effect

DOE PAGES

Cui, Hantao; Li, Fangxing; Fang, Xin; ...

2017-10-04

Our paper deals with extended-term energy storage (ES) arbitrage problems to maximize the annual revenue in deregulated power systems with high penetration wind power. The conventional ES arbitrage model takes the locational marginal prices (LMP) as an input and is unable to account for the impacts of ES operations on system LMPs. This paper proposes a bi-level ES arbitrage model, where the upper level maximizes the ES arbitrage revenue and the lower level simulates the market clearing process considering wind power and ES. The bi-level model is formulated as a mathematical program with equilibrium constraints (MPEC) and then recast intomore » a mixed-integer linear programming (MILP) using strong duality theory. Wind power fluctuations are characterized by the GARCH forecast model and the forecast error is modeled by forecast-bin based Beta distributions. Case studies are performed on a modified PJM 5-bus system and an IEEE 118-bus system with a weekly time horizon over an annual term to show the validity of the proposed bi-level model. The results from the conventional model and the bi-level model are compared under different ES power and energy ratings, and also various load and wind penetration levels.« less

Forecasts of health care utilization related to pandemic A(H1N1)2009 influenza in the Nord-Pas-de-Calais region, France.

PubMed

Giovannelli, J; Loury, P; Lainé, M; Spaccaferri, G; Hubert, B; Chaud, P

2015-05-01

To describe and evaluate the forecasts of the load that pandemic A(H1N1)2009 influenza would have on the general practitioners (GP) and hospital care systems, especially during its peak, in the Nord-Pas-de-Calais (NPDC) region, France. Modelling study. The epidemic curve was modelled using an assumption of normal distribution of cases. The values for the forecast parameters were estimated from a literature review of observed data from the Southern hemisphere and French Overseas Territories, where the pandemic had already occurred. Two scenarios were considered, one realistic, the other pessimistic, enabling the authors to evaluate the 'reasonable worst case'. Forecasts were then assessed by comparing them with observed data in the NPDC region--of 4 million people. The realistic scenarios forecasts estimated 300,000 cases, 1500 hospitalizations, 225 intensive care units (ICU) admissions for the pandemic wave; 115 hospital beds and 45 ICU beds would be required per day during the peak. The pessimistic scenario's forecasts were 2-3 times higher than the realistic scenario's forecasts. Observed data were: 235,000 cases, 1585 hospitalizations, 58 ICU admissions; and a maximum of 11.6 ICU beds per day. The realistic scenario correctly estimated the temporal distribution of GP and hospitalized cases but overestimated the number of cases admitted to ICU. Obtaining more robust data for parameters estimation--particularly the rate of ICU admission among the population that the authors recommend to use--may provide better forecasts. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Analysis of continuous GPS measurements from southern Victoria Land, Antarctica

USGS Publications Warehouse

Willis, Michael J.

2007-01-01

Several years of continuous data have been collected at remote bedrock Global Positioning System (GPS) sites in southern Victoria Land, Antarctica. Annual to sub-annual variations are observed in the position time-series. An atmospheric pressure loading (APL) effect is calculated from pressure field anomalies supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) model loading an elastic Earth model. The predicted APL signal has a moderate correlation with the vertical position time-series at McMurdo, Ross Island (International Global Navigation Satellite System Service (IGS) station MCM4), produced using a global solution. In contrast, a local solution in which MCM4 is the fiducial site generates a vertical time series for a remote site in Victoria Land (Cape Roberts, ROB4) which exhibits a low, inverse correlation with the predicted atmospheric pressure loading signal. If, in the future, known and well modeled geophysical loads can be separated from the time-series, then local hydrological loading, of interest for glaciological and climate applications, can potentially be extracted from the GPS time-series.

Managing the space-time-load continuum in TMDL planning: A case study for understanding groundwater loads through advanced mapping techniques

EPA Science Inventory

The lag time between groundwater recharge and discharge in a watershed and the potential groundwater load to streams is an important factor in forecasting responses to future land use practices. We call this concept managing the “space-time-load continuum”. It’s understood that i...

An evaluation of the impact of biomass burning smoke aerosol particles on near surface temperature forecasts

NASA Astrophysics Data System (ADS)

Zhang, J.; Reid, J. S.; Benedetti, A.; Christensen, M.; Marquis, J. W.

2016-12-01

Currently, with the improvements in aerosol forecast accuracies through aerosol data assimilation, the community is unavoidably facing a scientific question: is it worth the computational time to insert real-time aerosol analyses into numerical models for weather forecasts? In this study, by analyzing a significant biomass burning aerosol event that occurred in 2015 over the Northern part of the Central US, the impact of aerosol particles on near-surface temperature forecasts is evaluated. The aerosol direct surface cooling efficiency, which links surface temperature changes to aerosol loading, is derived from observational-based data for the first time. The potential of including real-time aerosol analyses into weather forecasting models for near surface temperature forecasts is also investigated.

On-line algorithms for forecasting hourly loads of an electric utility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vemuri, S.; Huang, W.L.; Nelson, D.J.

A method that lends itself to on-line forecasting of hourly electric loads is presented, and the results of its use are compared to models developed using the Box-Jenkins method. The method consits of processing the historical hourly loads with a sequential least-squares estimator to identify a finite-order autoregressive model which, in turn, is used to obtain a parsimonious autoregressive-moving average model. The method presented has several advantages in comparison with the Box-Jenkins method including much-less human intervention, improved model identification, and better results. The method is also more robust in that greater confidence can be placed in the accuracy ofmore » models based upon the various measures available at the identification stage.« less

Chesapeake Bay Hypoxic Volume Forecasts and Results

USGS Publications Warehouse

Evans, Mary Anne; Scavia, Donald

2013-01-01

Given the average Jan-May 2013 total nitrogen load of 162,028 kg/day, this summer's hypoxia volume forecast is 6.1 km3, slightly smaller than average size for the period of record and almost the same as 2012. The late July 2013 measured volume was 6.92 km3.

Regional PV power estimation and forecast to mitigate the impact of high photovoltaic penetration on electric grid.

NASA Astrophysics Data System (ADS)

Pierro, Marco; De Felice, Matteo; Maggioni, Enrico; Moser, David; Perotto, Alessandro; Spada, Francesco; Cornaro, Cristina

2017-04-01

The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid and increase the amount of energy reserve and the energy imbalance cost. On regional scale, solar power estimation and forecast is becoming essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators of generation. Indeed the estimation of regional PV power can be used for PV power supervision and real time control of residual load. Mid-term PV power forecast can be employed for transmission scheduling to reduce energy imbalance and related cost of penalties, residual load tracking, trading optimization, secondary energy reserve assessment. In this context, a new upscaling method was developed and used for estimation and mid-term forecast of the photovoltaic distributed generation in a small area in the north of Italy under the control of a local DSO. The method was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. It requires a low computational effort and very few input information should be provided by users. The power estimation model achieved a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% while the one and two days forecast achieve to a RMSE of 7% and 7.5%. A model to estimate the forecast error and the prediction intervals was also developed. The photovoltaic production in the considered region provided the 6.9% of the electric consumption in 2015. Since the PV penetration is very similar to the one observed at national level (7.9%), this is a good case study to analyse the impact of PV generation on the electric grid and the effects of PV power forecast on transmission scheduling and on secondary reserve estimation. It appears that, already with 7% of PV penetration, the distributed PV generation could have a great impact both on the DSO energy need and on the transmission scheduling capability. Indeed, for some hours of the days in summer time, the photovoltaic generation can provide from 50% to 75% of the energy that the local DSO should buy from Italian TSO to cover the electrical demand. Moreover, mid-term forecast can reduce the annual energy imbalance between the scheduled transmission and the actual one from 10% of the TSO energy supply (without considering the PV forecast) to 2%. Furthermore, it was shown that prediction intervals could be used not only to estimate the probability of a specific PV generation bid on the energy market, but also to reduce the energy reserve predicted for the next day. Two different methods for energy reserve estimation were developed and tested. The first is based on a clear sky model while the second makes use of the PV prediction intervals with the 95% of confidence level. The latter reduces the amount of the day-ahead energy reserve of 36% with respect the clear sky method.

Analog-Based Postprocessing of Navigation-Related Hydrological Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Hemri, S.; Klein, B.

2017-11-01

Inland waterway transport benefits from probabilistic forecasts of water levels as they allow to optimize the ship load and, hence, to minimize the transport costs. Probabilistic state-of-the-art hydrologic ensemble forecasts inherit biases and dispersion errors from the atmospheric ensemble forecasts they are driven with. The use of statistical postprocessing techniques like ensemble model output statistics (EMOS) allows for a reduction of these systematic errors by fitting a statistical model based on training data. In this study, training periods for EMOS are selected based on forecast analogs, i.e., historical forecasts that are similar to the forecast to be verified. Due to the strong autocorrelation of water levels, forecast analogs have to be selected based on entire forecast hydrographs in order to guarantee similar hydrograph shapes. Custom-tailored measures of similarity for forecast hydrographs comprise hydrological series distance (SD), the hydrological matching algorithm (HMA), and dynamic time warping (DTW). Verification against observations reveals that EMOS forecasts for water level at three gauges along the river Rhine with training periods selected based on SD, HMA, and DTW compare favorably with reference EMOS forecasts, which are based on either seasonal training periods or on training periods obtained by dividing the hydrological forecast trajectories into runoff regimes.

The promise of air cargo: System aspects and vehicle design

NASA Technical Reports Server (NTRS)

Whitehead, A. H., Jr.

1976-01-01

The current operation of the air cargo system is reviewed. An assessment of the future of air cargo is provided by: (1) analyzing statistics and trends, (2) by noting system problems and inefficiencies, (3) by analyzing characteristics of 'air eligible' commodities, and (4) by showing the promise of new technology for future cargo aircraft with significant improvements in costs and efficiency. The following topics are discussed: (1) air cargo demand forecasts; (2) economics of air cargo transport; (3) the integrated air cargo system; (4) evolution of airfreighter design; and (5) the span distributed load concept.

Efficient operation scheduling for adsorption chillers using predictive optimization-based control methods

NASA Astrophysics Data System (ADS)

Bürger, Adrian; Sawant, Parantapa; Bohlayer, Markus; Altmann-Dieses, Angelika; Braun, Marco; Diehl, Moritz

2017-10-01

Within this work, the benefits of using predictive control methods for the operation of Adsorption Cooling Machines (ACMs) are shown on a simulation study. Since the internal control decisions of series-manufactured ACMs often cannot be influenced, the work focuses on optimized scheduling of an ACM considering its internal functioning as well as forecasts for load and driving energy occurrence. For illustration, an assumed solar thermal climate system is introduced and a system model suitable for use within gradient-based optimization methods is developed. The results of a system simulation using a conventional scheme for ACM scheduling are compared to the results of a predictive, optimization-based scheduling approach for the same exemplary scenario of load and driving energy occurrence. The benefits of the latter approach are shown and future actions for application of these methods for system control are addressed.

Optimization modeling of U.S. renewable electricity deployment using local input variables

NASA Astrophysics Data System (ADS)

Bernstein, Adam

For the past five years, state Renewable Portfolio Standard (RPS) laws have been a primary driver of renewable electricity (RE) deployments in the United States. However, four key trends currently developing: (i) lower natural gas prices, (ii) slower growth in electricity demand, (iii) challenges of system balancing intermittent RE within the U.S. transmission regions, and (iv) fewer economical sites for RE development, may limit the efficacy of RPS laws over the remainder of the current RPS statutes' lifetime. An outsized proportion of U.S. RE build occurs in a small number of favorable locations, increasing the effects of these variables on marginal RE capacity additions. A state-by-state analysis is necessary to study the U.S. electric sector and to generate technology specific generation forecasts. We used LP optimization modeling similar to the National Renewable Energy Laboratory (NREL) Renewable Energy Development System (ReEDS) to forecast RE deployment across the 8 U.S. states with the largest electricity load, and found state-level RE projections to Year 2031 significantly lower than thoseimplied in the Energy Information Administration (EIA) 2013 Annual Energy Outlook forecast. Additionally, the majority of states do not achieve their RPS targets in our forecast. Combined with the tendency of prior research and RE forecasts to focus on larger national and global scale models, we posit that further bottom-up state and local analysis is needed for more accurate policy assessment, forecasting, and ongoing revision of variables as parameter values evolve through time. Current optimization software eliminates much of the need for algorithm coding and programming, allowing for rapid model construction and updating across many customized state and local RE parameters. Further, our results can be tested against the empirical outcomes that will be observed over the coming years, and the forecast deviation from the actuals can be attributed to discrete parameter variances.

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input variable of different ANN structures [3]. The performance of each ANN structure is evaluated by the voting analysis based on eleven criteria, which are the root mean square error (RMSE), the correlation index (R), the mean absolute percentage error (MAPE), the mean percentage error (MPE), the mean percentage error (ME), the percentage volume in errors (VE), the percentage error in peak (MF), the normalized mean bias error (NMBE), the normalized root mean bias error (NRMSE), the Nash-Sutcliffe model efficiency coefficient (E) and the modified Nash-Sutcliffe model efficiency coefficient (E1). The next day flow for the test set is calculated using the best ANN structure's model. Consequently, the confidence intervals of various confidence levels for training, evaluation and test sets are compared in order to explore the generalisation dynamics of confidence intervals from training and evaluation sets. [1] H.S. Hippert, C.E. Pedreira, R.C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. on Power Systems, vol. 16, no. 1, 2001, pp. 44-55. [2] G. J. Tsekouras, N.E. Mastorakis, F.D. Kanellos, V.T. Kontargyri, C.D. Tsirekis, I.S. Karanasiou, Ch.N. Elias, A.D. Salis, P.A. Kontaxis, A.A. Gialketsi: "Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor", WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, (CSECS '10), Vouliagmeni, Athens, Greece, December 29-31, 2010. [3] D. Panagoulia, I. Trichakis, G. J. Tsekouras: "Flow Forecasting via Artificial Neural Networks - A Study for Input Variables conditioned on atmospheric circulation", European Geosciences Union, General Assembly 2012 (NH1.1 / AS1.16 - Extreme meteorological and hydrological events induced by severe weather and climate change), Vienna, Austria, 22-27 April 2012.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.

Economic Analysis Case Studies of Battery Energy Storage with SAM

DOE Office of Scientific and Technical Information (OSTI.GOV)

DiOrio, Nicholas; Dobos, Aron; Janzou, Steven

2015-11-01

Interest in energy storage has continued to increase as states like California have introduced mandates and subsidies to spur adoption. This energy storage includes customer sited behind-the-meter storage coupled with photovoltaics (PV). This paper presents case study results from California and Tennessee, which were performed to assess the economic benefit of customer-installed systems. Different dispatch strategies, including manual scheduling and automated peak-shaving were explored to determine ideal ways to use the storage system to increase the system value and mitigate demand charges. Incentives, complex electric tariffs, and site specific load and PV data were used to perform detailed analysis. Themore » analysis was performed using the free, publically available System Advisor Model (SAM) tool. We find that installation of photovoltaics with a lithium-ion battery system priced at $300/kWh in Los Angeles under a high demand charge utility rate structure and dispatched using perfect day-ahead forecasting yields a positive net-present value, while all other scenarios cost the customer more than the savings accrued. Different dispatch strategies, including manual scheduling and automated peak-shaving were explored to determine ideal ways to use the storage system to increase the system value and mitigate demand charges. Incentives, complex electric tariffs, and site specific load and PV data were used to perform detailed analysis. The analysis was performed using the free, publically available System Advisor Model (SAM) tool. We find that installation of photovoltaics with a lithium-ion battery system priced at $300/kWh in Los Angeles under a high demand charge utility rate structure and dispatched using perfect day-ahead forecasting yields a positive net-present value, while all other scenarios cost the customer more than the savings accrued.« less

Volcanic Ash Data Assimilation System for Atmospheric Transport Model

NASA Astrophysics Data System (ADS)

Ishii, K.; Shimbori, T.; Sato, E.; Tokumoto, T.; Hayashi, Y.; Hashimoto, A.

2017-12-01

The Japan Meteorological Agency (JMA) has two operations for volcanic ash forecasts, which are Volcanic Ash Fall Forecast (VAFF) and Volcanic Ash Advisory (VAA). In these operations, the forecasts are calculated by atmospheric transport models including the advection process, the turbulent diffusion process, the gravitational fall process and the deposition process (wet/dry). The initial distribution of volcanic ash in the models is the most important but uncertain factor. In operations, the model of Suzuki (1983) with many empirical assumptions is adopted to the initial distribution. This adversely affects the reconstruction of actual eruption plumes.We are developing a volcanic ash data assimilation system using weather radars and meteorological satellite observation, in order to improve the initial distribution of the atmospheric transport models. Our data assimilation system is based on the three-dimensional variational data assimilation method (3D-Var). Analysis variables are ash concentration and size distribution parameters which are mutually independent. The radar observation is expected to provide three-dimensional parameters such as ash concentration and parameters of ash particle size distribution. On the other hand, the satellite observation is anticipated to provide two-dimensional parameters of ash clouds such as mass loading, top height and particle effective radius. In this study, we estimate the thickness of ash clouds using vertical wind shear of JMA numerical weather prediction, and apply for the volcanic ash data assimilation system.

Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service.

PubMed

Bastl, Katharina; Berger, Uwe; Kmenta, Maximilian

2017-05-08

Pollen forecasts are highly valuable for allergen avoidance and thus raising the quality of life of persons concerned by pollen allergies. They are considered as valuable free services for the public. Careful scientific evaluation of pollen forecasts in terms of accurateness and reliability has not been available till date. The aim of this study was to analyze 9 mobile apps, which deliver pollen information and pollen forecasts, with a focus on their accurateness regarding the prediction of the pollen load in the grass pollen season 2016 to assess their usefulness for pollen allergy sufferers. The following number of apps was evaluated for each location: 3 apps for Vienna (Austria), 4 apps for Berlin (Germany), and 1 app each for Basel (Switzerland) and London (United Kingdom). All mobile apps were freely available. Today's grass pollen forecast was compared throughout the defined grass pollen season at each respective location with measured grass pollen concentrations. Hit rates were calculated for the exact performance and for a tolerance in a range of ±2 and ±4 pollen per cubic meter. In general, for most apps, hit rates score around 50% (6 apps). It was found that 1 app showed better results, whereas 3 apps performed less well. Hit rates increased when calculated with tolerances for most apps. In contrast, the forecast for the "readiness to flower" for grasses was performed at a sufficiently accurate level, although only two apps provided such a forecast. The last of those forecasts coincided with the first moderate grass pollen load on the predicted day or 3 days after and performed even from about a month before well within the range of 3 days. Advertisement was present in 3 of the 9 analyzed apps, whereas an imprint mentioning institutions with experience in pollen forecasting was present in only three other apps. The quality of pollen forecasts is in need of improvement, and quality control for pollen forecasts is recommended to avoid potential harm to pollen allergy sufferers due to inadequate forecasts. The inclusion of information on reliability of provided forecasts and a similar handling regarding probabilistic weather forecasts should be considered. ©Katharina Bastl, Uwe Berger, Maximilian Kmenta. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.05.2017.

Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service

PubMed Central

Berger, Uwe; Kmenta, Maximilian

2017-01-01

Background Pollen forecasts are highly valuable for allergen avoidance and thus raising the quality of life of persons concerned by pollen allergies. They are considered as valuable free services for the public. Careful scientific evaluation of pollen forecasts in terms of accurateness and reliability has not been available till date. Objective The aim of this study was to analyze 9 mobile apps, which deliver pollen information and pollen forecasts, with a focus on their accurateness regarding the prediction of the pollen load in the grass pollen season 2016 to assess their usefulness for pollen allergy sufferers. Methods The following number of apps was evaluated for each location: 3 apps for Vienna (Austria), 4 apps for Berlin (Germany), and 1 app each for Basel (Switzerland) and London (United Kingdom). All mobile apps were freely available. Today’s grass pollen forecast was compared throughout the defined grass pollen season at each respective location with measured grass pollen concentrations. Hit rates were calculated for the exact performance and for a tolerance in a range of ±2 and ±4 pollen per cubic meter. Results In general, for most apps, hit rates score around 50% (6 apps). It was found that 1 app showed better results, whereas 3 apps performed less well. Hit rates increased when calculated with tolerances for most apps. In contrast, the forecast for the “readiness to flower” for grasses was performed at a sufficiently accurate level, although only two apps provided such a forecast. The last of those forecasts coincided with the first moderate grass pollen load on the predicted day or 3 days after and performed even from about a month before well within the range of 3 days. Advertisement was present in 3 of the 9 analyzed apps, whereas an imprint mentioning institutions with experience in pollen forecasting was present in only three other apps. Conclusions The quality of pollen forecasts is in need of improvement, and quality control for pollen forecasts is recommended to avoid potential harm to pollen allergy sufferers due to inadequate forecasts. The inclusion of information on reliability of provided forecasts and a similar handling regarding probabilistic weather forecasts should be considered. PMID:28483740

Performance of Optimization Heuristics for the Operational Planning of Multi-energy Storage Systems

NASA Astrophysics Data System (ADS)

Haas, J.; Schradi, J.; Nowak, W.

2016-12-01

In the transition to low-carbon energy sources, energy storage systems (ESS) will play an increasingly important role. Particularly in the context of solar power challenges (variability, uncertainty), ESS can provide valuable services: energy shifting, ramping, robustness against forecast errors, frequency support, etc. However, these qualities are rarely modelled in the operational planning of power systems because of the involved computational burden, especially when multiple ESS technologies are involved. This work assesses two optimization heuristics for speeding up the optimal operation problem. It compares their accuracy (in terms of costs) and speed against a reference solution. The first heuristic (H1) is based on a merit order. Here, the ESS are sorted from lower to higher operational costs (including cycling costs). For each time step, the cheapest available ESS is used first, followed by the second one and so on, until matching the net load (demand minus available renewable generation). The second heuristic (H2) uses the Fourier transform to detect the main frequencies that compose the net load. A specific ESS is assigned to each frequency range, aiming to smoothen the net load. Finally, the reference solution is obtained with a mixed integer linear program (MILP). H1, H2 and MILP are subject to technical constraints (energy/power balance, ramping rates, on/off states...). Costs due to operation, replacement (cycling) and unserved energy are considered. Four typical days of a system with a high share of solar energy were used in several test cases, varying the resolution from one second to fifteen minutes. H1 and H2 achieve accuracies of about 90% and 95% in average, and speed-up times of two to three and one to two orders of magnitude, respectively. The use of the heuristics looks promising in the context of planning the expansion of power systems, especially when their loss of accuracy is outweighed by solar or wind forecast errors.

Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

2016-09-01

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Time Relevance of Convective Weather Forecast for Air Traffic Automation

NASA Technical Reports Server (NTRS)

Chan, William N.

2006-01-01

The Federal Aviation Administration (FAA) is handling nearly 120,000 flights a day through its Air Traffic Management (ATM) system and air traffic congestion is expected to increse substantially over the next 20 years. Weather-induced impacts to throughput and efficiency are the leading cause of flight delays accounting for 70% of all delays with convective weather accounting for 60% of all weather related delays. To support the Next Generation Air Traffic System goal of operating at 3X current capacity in the NAS, ATC decision support tools are being developed to create advisories to assist controllers in all weather constraints. Initial development of these decision support tools did not integrate information regarding weather constraints such as thunderstorms and relied on an additional system to provide that information. Future Decision Support Tools should move towards an integrated system where weather constraints are factored into the advisory of a Decision Support Tool (DST). Several groups such at NASA-Ames, Lincoln Laboratories, and MITRE are integrating convective weather data with DSTs. A survey of current convective weather forecast and observation data show they span a wide range of temporal and spatial resolutions. Short range convective observations can be obtained every 5 mins with longer range forecasts out to several days updated every 6 hrs. Today, the short range forecasts of less than 2 hours have a temporal resolution of 5 mins. Beyond 2 hours, forecasts have much lower temporal. resolution of typically 1 hour. Spatial resolutions vary from 1km for short range to 40km for longer range forecasts. Improving the accuracy of long range convective forecasts is a major challenge. A report published by the National Research Council states improvements for convective forecasts for the 2 to 6 hour time frame will only be achieved for a limited set of convective phenomena in the next 5 to 10 years. Improved longer range forecasts will be probabilistic as opposed to the deterministic shorter range forecasts. Despite the known low level of confidence with respect to long range convective forecasts, these data are still useful to a DST routing algorithm. It is better to develop an aircraft route using the best information available than no information. The temporally coarse long range forecast data needs to be interpolated to be useful to a DST. A DST uses aircraft trajectory predictions that need to be evaluated for impacts by convective storms. Each time-step of a trajectory prediction n&s to be checked against weather data. For the case of coarse temporal data, there needs to be a method fill in weather data where there is none. Simply using the coarse weather data without any interpolation can result in DST routes that are impacted by regions of strong convection. Increasing the temporal resolution of these data can be achieved but result in a large dataset that may prove to be an operational challenge in transmission and loading by a DST. Currently, it takes about 7mins retrieve a 7mb RUC2 forecast file from NOAA at NASA-Ames Research Center. A prototype NCWF6 1 hour forecast is about 3mb in size. A Six hour NCWFG forecast with a 1hr forecast time-step will be about l8mb (6 x 3mb). A 6 hour NCWF6 forecast with a l5min forecast time-step will be about 7mb (24 x 3mb). Based on the time it takes to retrieve a 7mb RUC2 forecast, it will take approximately 70mins to retrieve a 6 hour NCWF forecast with 15min time steps. Until those issues are addressed, there is a need to develop an algorithm that interpolates between these temporally coarse long range forecasts. This paper describes a method of how to use low temporal resolution probabilistic weather forecasts in a DST. The beginning of this paper is a description of some convective weather forecast and observation products followed by an example of how weather data are used by a DST. The subsequent sections will describe probabilistic forecasts followed by a descrtion of a method to use low temporal resolution probabilistic weather forecasts by providing a relevance value to these data outside of their valid times.

Online Analysis of Wind and Solar Part I: Ramping Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etingov, Pavel V.; Ma, Jian; Makarov, Yuri V.

2012-01-31

To facilitate wider penetration of renewable resources without compromising system reliability concerns arising from the lack of predictability of intermittent renewable resources, a tool for use by California Independent System Operator (CAISO) power grid operators was developed by Pacific Northwest National Laboratory (PNNL) in conjunction with CAISO with funding from California Energy Commission. This tool predicts and displays additional capacity and ramping requirements caused by uncertainties in forecasts of loads and renewable generation. The tool is currently operational in the CAISO operations center. This is one of two final reports on the project.

Self-heating forecasting for thick laminate specimens in fatigue

NASA Astrophysics Data System (ADS)

Lahuerta, F.; Westphal, T.; Nijssen, R. P. L.

2014-12-01

Thick laminate sections can be found from the tip to the root in most common wind turbine blade designs. Obtaining accurate and reliable design data for thick laminates is subject of investigations, which include experiments on thick laminate coupons. Due to the poor thermal conductivity properties of composites and the material self-heating that occurs during the fatigue loading, high temperature gradients may appear through the laminate thickness. In the case of thick laminates in high load regimes, the core temperature might influence the mechanical properties, leading to premature failures. In the present work a method to forecast the self-heating of thick laminates in fatigue loading is presented. The mechanical loading is related with the laminate self-heating, via the cyclic strain energy and the energy loss ratio. Based on this internal volumetric heat load a thermal model is built and solved to obtain the temperature distribution in the transient state. Based on experimental measurements of the energy loss factor for 10mm thick coupons, the method is described and the resulting predictions are compared with experimental surface temperature measurements on 10 and 30mm UD thick laminate specimens.

An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting.

PubMed

Hippert, Henrique S; Taylor, James W

2010-04-01

Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation. Copyright 2009 Elsevier Ltd. All rights reserved.

Efficient Resources Provisioning Based on Load Forecasting in Cloud

PubMed Central

Hu, Rongdong; Jiang, Jingfei; Liu, Guangming; Wang, Lixin

2014-01-01

Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements. PMID:24701160

Forecasting electricity usage using univariate time series models

NASA Astrophysics Data System (ADS)

Hock-Eam, Lim; Chee-Yin, Yip

2014-12-01

Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.

Spring thaw predictor & development of real time spring load restrictions.

DOT National Transportation Integrated Search

2011-02-01

This report summarizes the results of a study to develop a correlation between weather forecasts and the : spring thaw in order to reduce the duration of load limits on New Hampshire roadways. The study used a falling : weight deflectometer at 10 sit...

Managing the space-time-load continuum in TMDL planning: a case study for understanding groundwaer loads through advanced mapping techniques

Treesearch

Phillip Harte; Marcel Belaval; Andrea Traviglia

2016-01-01

The lag time between groundwater recharge and discharge in a watershed and the potential groundwater load to streams is an important factor in forecasting responses to future land use practices. We call this concept managing the “space-time-load continuum.” It’s understood that in any given watershed, the response function (the load at any given time) will differ for...

Four dimensional variational assimilation of in-situ and remote-sensing aerosol data

NASA Astrophysics Data System (ADS)

Nieradzik, L. P.; Elbern, H.

2012-04-01

Aerosols play an increasingly important role in atmospheric modelling. They have a strong influence on the radiative transfer balance and a significant impact on human health. Their origin is various and so are its effects. Most of the measurement sites in Europe account for an integrated aerosol load PMx (Particulate Matter of less than x μm in diameter) which does not give any qualitative information on the composition of the aerosol. Since very different constituents contribute to PMx, like e.g. mineral dust derived from desert storms or sea salt, it is necessary to make aerosol forecasts not only of load, but also type resolved. The method of four dimensional variational data assimilation (4Dvar) is a widely known technique to enhance forecast skills of CTMs (Chemistry-Transport-Models) by ingesting in-situ and, especially, remote-sensing measurements. The EURAD-IM (EURopean Air pollution Dispersion - Inverse Model), containing a full adjoint gas-phase model, has been expanded with an adjoint of the MADE (Modal Aerosol Dynamics model for Europe) to optimise initial and boundary values for aerosols using 4Dvar. A forward and an adjoint radiative transfer model is driven by the EURAD-IM as mapping between BLAOT (Boundary Layer Aerosol Optical Thickness) and internal aerosol species. Furthermore, its condensation scheme has been bypassed by an HDMR (High-Dimensional-Model-Representation) to ensure differentiability. In this study both in-situ measured PMx as well as satellite retrieved aerosol optical thicknesses have been assimilated and the effect on forecast performance has been investigated. The source of BLAOT is the aerosol retrieval system SYNAER (SYNergetic AErosol Retrieval) from DLR-DFD that retrieves AOT by making use of both AATSR/SCIAMACHY and AVHRR/GOME-2 data respectively. Its strengths are a large spatial coverage, near real-time availability, and the classification of five intrinsic aerosol species, namely water-solubles, water-insolubles, soot, sea salt, and mineral dust which are furthermore size resolved in terms of modes. The skill of the aerosol 4Dvar system was tested in two episodes: 1) July through August 2003, a dry period with strong wildfire activity in Europe, and 2) October through November 2008, the period of the ZEPTER-2 (Second ZEPpelin based Tropospheric photochemical chemistry expERiment) measurement campaign in the area of Lake Constance. In the latter case one-way nesting has been applied from a horizontal grid resolution of 45 km down to 5 km. Overall, the results showed a significant increase in forecast quality of tropospheric aerosol loads.

Forecasting Strategies for Predicting Peak Electric Load Days

NASA Astrophysics Data System (ADS)

Saxena, Harshit

Academic institutions spend thousands of dollars every month on their electric power consumption. Some of these institutions follow a demand charges pricing structure; here the amount a customer pays to the utility is decided based on the total energy consumed during the month, with an additional charge based on the highest average power load required by the customer over a moving window of time as decided by the utility. Therefore, it is crucial for these institutions to minimize the time periods where a high amount of electric load is demanded over a short duration of time. In order to reduce the peak loads and have more uniform energy consumption, it is imperative to predict when these peaks occur, so that appropriate mitigation strategies can be developed. The research work presented in this thesis has been conducted for Rochester Institute of Technology (RIT), where the demand charges are decided based on a 15 minute sliding window panned over the entire month. This case study makes use of different statistical and machine learning algorithms to develop a forecasting strategy for predicting the peak electric load days of the month. The proposed strategy was tested for a whole year starting May 2015 to April 2016 during which a total of 57 peak days were observed. The model predicted a total of 74 peak days during this period, 40 of these cases were true positives, hence achieving an accuracy level of 70 percent. The results obtained with the proposed forecasting strategy are promising and demonstrate an annual savings potential worth about $80,000 for a single submeter of RIT.

Reassessing hypoxia forecasts for the Gulf of Mexico.

PubMed

Scavia, Donald; Donnelly, Kristina A

2007-12-01

Gulf of Mexico hypoxia has received considerable scientific and policy attention because of its potential ecological and economic impacts and implications for agriculture within its massive watershed. A 2000 assessment concluded that increased nitrate load to the Gulf since the 1950s was the primary cause of large-scale hypoxia areas. More recently, models have suggested that large-scale hypoxia did not start untilthe mid-1970s, and that a 40-45% nitrogen load reduction may be needed to reach the hypoxia area goal of the Hypoxia Action Plan. Recently, USGS revised nutrient load estimates to the Gulf, and the Action Plan reassessment has questioned the role of phosphorus versus nitrogen in controlling hypoxia. In this paper, we re-evaluate model simulations, hindcasts, and forecasts using revised nitrogen loads, and testthe ability of a phosphorus-driven version of the model to reproduce hypoxia trends. Our analysis suggests that, if phosphorus is limiting now, it became so because of relative increases in nitrogen loads during the 1970s and 1980s. While our model suggests nitrogen load reductions of 37-45% or phosphorus load reductions of 40-50% below the 1980-1996 average are needed, we caution that a phosphorus-only strategy is potentially dangerous, and suggest it would be prudent to reduce both.

7 CFR 1710.203 - Requirement to prepare a load forecast-distribution borrowers.

Code of Federal Regulations, 2011 CFR

2011-01-01

...—distribution borrowers. (a) A distribution borrower that is a member of a power supply borrower with a total... forecast work plan of its power supply borrower. (b) A distribution borrower that is a member of a power supply borrower which is itself a member of another power supply borrower that has a total utility plant...

Economic evaluation of a solar hot-water-system

NASA Technical Reports Server (NTRS)

1981-01-01

Analysis shows economic benefits at six representative sites using actual data from Tempe, Arizona and San Diego, California installations. Model is two-tank cascade water heater with flat-plate collector array for single-family residences. Performances are forecast for Albuquerque, New Mexico; Fort Worth, Texas; Madison, Wisconsin; and Washington, D.C. Costs are compared to net energy savings using variables for each site's environmental conditions, loads, fuel costs, and other economic factors; uncertainty analysis is included.

An Example of Unsupervised Networks Kohonen's Self-Organizing Feature Map

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

Kohonen's self-organizing feature map belongs to a class of unsupervised artificial neural network commonly referred to as topographic maps. It serves two purposes, the quantization and dimensionality reduction of date. A short description of its history and its biological context is given. We show that the inherent classification properties of the feature map make it a suitable candidate for solving the classification task in power system areas like load forecasting, fault diagnosis and security assessment.

Using Haines Index coupled with fire weather model predicted from high resolution LAM forecasts to asses wildfire extreme behaviour in Southern Europe.

NASA Astrophysics Data System (ADS)

Gaetani, Francesco; Baptiste Filippi, Jean; Simeoni, Albert; D'Andrea, Mirko

2010-05-01

Haines Index (HI) was developed by USDA Forest Service to measure the atmosphere's contribution to the growth potential of a wildfire. The Haines Index combines two atmospheric factors that are known to have an effect on wildfires: Stability and Dryness. As operational tools, HI proved its ability to predict plume dominated high intensity wildfires. However, since HI does not take into account the fuel continuity, composition and moisture conditions and the effects of wind and topography on fire behaviour, its use as forecasting tool should be carefully considered. In this work we propose the use of HI, predicted from HR Limited Area Model forecasts, coupled with a Fire Weather model (i.e., RISICO system) fully operational in Italy since 2003. RISICO is based on dynamic models able to represent in space and in time the effects that environment and vegetal physiology have on fuels and, in turn, on the potential behaviour of wildfires. The system automatically acquires from remote databases a thorough data-set of input information both of in situ and spatial nature. Meteorological observations, radar data, Limited Area Model weather forecasts, EO data, and fuel data are managed by a Unified Interface able to process a wide set of different data. Specific semi-physical models are used in the system to simulate the dynamics of the fuels (load and moisture contents of dead and live fuel) and the potential fire behaviour (rate of spread and linear intensity). A preliminary validation of this approach will be provided with reference to Sardinia and Corsica Islands, two major islands of the Mediterranean See frequently affected by extreme plume dominated wildfires. A time series of about 3000 wildfires burnt in Sardinia and Corsica in 2007 and 2008 will be used to evaluate the capability of HI coupled with the outputs of the Fire Weather model to forecast the actual risk in time and in space.

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

Forecasting and Evaluation of Gas Pipelines Geometric Forms Breach Hazard

NASA Astrophysics Data System (ADS)

Voronin, K. S.

2016-10-01

Main gas pipelines during operation are under the influence of the permanent pressure drops which leads to their lengthening and as a result, to instability of their position in space. In dynamic systems that have feedback, phenomena, preceding emergencies, should be observed. The article discusses the forced vibrations of the gas pipeline cylindrical surface under the influence of dynamic loads caused by pressure surges, and the process of its geometric shape deformation. Frequency of vibrations, arising in the pipeline at the stage preceding its bending, is being determined. Identification of this frequency can be the basis for the development of a method of monitoring the technical condition of the gas pipeline, and forecasting possible emergency situations allows planning and carrying out in due time reconstruction works on sections of gas pipeline with a possible deviation from the design position.

A SIMPLE MODEL FOR FORECASTING THE EFFECTS OF NITROGEN LOADS ON CHESAPEAKE BAY HYPOXIA

EPA Science Inventory

The causes and consequences of oxygen depletion in Chesapeake Bay have been the focus of research, assessment, and policy action over the past several decades. An ongoing scientific re-evaluation of what nutrients load reductions are necessary to meet the water quality goals is ...

Using Information Processing Techniques to Forecast, Schedule, and Deliver Sustainable Energy to Electric Vehicles

NASA Astrophysics Data System (ADS)

Pulusani, Praneeth R.

As the number of electric vehicles on the road increases, current power grid infrastructure will not be able to handle the additional load. Some approaches in the area of Smart Grid research attempt to mitigate this, but those approaches alone will not be sufficient. Those approaches and traditional solution of increased power production can result in an insufficient and imbalanced power grid. It can lead to transformer blowouts, blackouts and blown fuses, etc. The proposed solution will supplement the ``Smart Grid'' to create a more sustainable power grid. To solve or mitigate the magnitude of the problem, measures can be taken that depend on weather forecast models. For instance, wind and solar forecasts can be used to create first order Markov chain models that will help predict the availability of additional power at certain times. These models will be used in conjunction with the information processing layer and bidirectional signal processing components of electric vehicle charging systems, to schedule the amount of energy transferred per time interval at various times. The research was divided into three distinct components: (1) Renewable Energy Supply Forecast Model, (2) Energy Demand Forecast from PEVs, and (3) Renewable Energy Resource Estimation. For the first component, power data from a local wind turbine, and weather forecast data from NOAA were used to develop a wind energy forecast model, using a first order Markov chain model as the foundation. In the second component, additional macro energy demand from PEVs in the Greater Rochester Area was forecasted by simulating concurrent driving routes. In the third component, historical data from renewable energy sources was analyzed to estimate the renewable resources needed to offset the energy demand from PEVs. The results from these models and components can be used in the smart grid applications for scheduling and delivering energy. Several solutions are discussed to mitigate the problem of overloading transformers, lack of energy supply, and higher utility costs.

Performance Assessment of the Spare Parts for the Activation of Relocated Systems (SPARES) Forecasting Model

DTIC Science & Technology

1991-09-01

constant data into the gaining base’s computer records. Among the data elements to be loaded, the 1XT434 image contains the level detail effective date...the mission support effective date, and the PBR override (19:19-203). In conjunction with the 1XT434, the Mission Change Parameter Image (Constant...the gaining base (19:19-208). The level detail effective date establishes the date the MCDDFR and MCDDR "are considered by the requirements computation

Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market

PubMed Central

2017-01-01

Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load. PMID:28426739

Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market.

PubMed

Bozkurt, Ömer Özgür; Biricik, Göksel; Tayşi, Ziya Cihan

2017-01-01

Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.

Integrating Solar Power onto the Electric Grid - Bridging the Gap between Atmospheric Science, Engineering and Economics

NASA Astrophysics Data System (ADS)

Ghonima, M. S.; Yang, H.; Zhong, X.; Ozge, B.; Sahu, D. K.; Kim, C. K.; Babacan, O.; Hanna, R.; Kurtz, B.; Mejia, F. A.; Nguyen, A.; Urquhart, B.; Chow, C. W.; Mathiesen, P.; Bosch, J.; Wang, G.

2015-12-01

One of the main obstacles to high penetrations of solar power is the variable nature of solar power generation. To mitigate variability, grid operators have to schedule additional reliability resources, at considerable expense, to ensure that load requirements are met by generation. Thus despite the cost of solar PV decreasing, the cost of integrating solar power will increase as penetration of solar resources onto the electric grid increases. There are three principal tools currently available to mitigate variability impacts: (i) flexible generation, (ii) storage, either virtual (demand response) or physical devices and (iii) solar forecasting. Storage devices are a powerful tool capable of ensuring smooth power output from renewable resources. However, the high cost of storage is prohibitive and markets are still being designed to leverage their full potential and mitigate their limitation (e.g. empty storage). Solar forecasting provides valuable information on the daily net load profile and upcoming ramps (increasing or decreasing solar power output) thereby providing the grid advance warning to schedule ancillary generation more accurately, or curtail solar power output. In order to develop solar forecasting as a tool that can be utilized by the grid operators we identified two focus areas: (i) develop solar forecast technology and improve solar forecast accuracy and (ii) develop forecasts that can be incorporated within existing grid planning and operation infrastructure. The first issue required atmospheric science and engineering research, while the second required detailed knowledge of energy markets, and power engineering. Motivated by this background we will emphasize area (i) in this talk and provide an overview of recent advancements in solar forecasting especially in two areas: (a) Numerical modeling tools for coastal stratocumulus to improve scheduling in the day-ahead California energy market. (b) Development of a sky imager to provide short term forecasts (0-20 min ahead) to improve optimization and control of equipment on distribution feeders with high penetration of solar. Leveraging such tools that have seen extensive use in the atmospheric sciences supports the development of accurate physics-based solar forecast models. Directions for future research are also provided.

Superensemble forecasts of dengue outbreaks

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2016-01-01

In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems. PMID:27733698

Forecasting future phosphorus export to the Laurentian Great Lakes from land-derived nutrient inputs

NASA Astrophysics Data System (ADS)

LaBeau, M. B.; Robertson, D. M.; Mayer, A. S.; Pijanowski, B. C.

2011-12-01

Anthropogenic use of the land through agricultural and urban activities has significantly increased phosphorus loading to rivers that flow to the Great Lakes. Phosphorus (P) is a critical element in the eutrophication of the freshwater ecosystems, most notably the Great Lakes. To better understand factors influencing phosphorus delivery to aquatic systems and thus their potential harmful effects to lake ecosystems, models that predict P export should incorporate account for changing changes in anthropogenic activities. Land-derived P from high yielding sources, such as agriculture and urban areas, affect eutrophication at various scales (e.g. specific bays to all of Lake Erie). SPARROW (SPAtially Referenced Regression On Watershed attributes) is a spatially explicit watershed model that has been used to understand linkages between land-derived sources and nutrient transport to the Great Lakes. The Great Lakes region is expected to experience a doubling of urbanized areas along with a ten percent increase in agricultural use over the next 40 years, which is likely to increase P loading. To determine how these changes will impact P loading, SPARROW have been developed that relate changes in land use to changes in nutrient sources, including relationships between row crop acreage and fertilizer intensity and urban land use and point source intensity. We used land use projections from the Land Transformation Model, a, spatially explicit, neural-net based land change model. Land use patterns from current to 2040 were used as input into HydroSPARROW, a forecasting tool that enables SPARROW to simulate the effects of various land-use and climate scenarios. Consequently, this work is focusing on understanding the effects of how specific agriculture and urbanization activities affect P loading in the watersheds of the Laurentian Great Lakes to potentially find strategies to reduce the extent and severity of future eutrophication.

The Future of Low-Carbon Electricity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenblatt, Jeffery B.; Brown, Nicholas R.; Slaybaugh, Rachel

Here, we review future global demand for electricity and major technologies positioned to supply itwith minimal greenhouse gas (GHG) emissions: renewables (wind, solar, water, geothermal and biomass), nuclear fission, and fossil power with CO 2 capture and sequestration. Two breakthrough technologies (space solar power and nuclear fusion) are discussed as exciting but uncertain additional options for low net GHG emissions (“low-carbon”) electricity generation. Grid integration technologies (monitoring and forecasting of transmission and distribution systems, demand-side load management, energy storage, and load balancing with low-carbon fuel substitutes) are also discussed. For each topic, recent historical trends and future prospects are reviewed,more » along with technical challenges, costs and other issues as appropriate. While no technology represents an ideal solution, their strengths can be enhanced by deployment in combination, along with grid integration that forms a critical set of enabling technologies to assure a reliable and robust future low-carbon electricity system.« less

The Future of Low-Carbon Electricity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenblatt, Jeffery B.; Brown, Nicholas R.; Slaybaugh, Rachel

We review future global demand for electricity and major technologies positioned to supply it with minimal greenhouse gas (GHG) emissions: renewables (wind, solar, water, geothermal, and biomass), nuclear fission, and fossil power with CO2 capture and sequestration. We discuss two breakthrough technologies (space solar power and nuclear fusion) as exciting but uncertain additional options for low-net GHG emissions (i.e., low-carbon) electricity generation. In addition, we discuss grid integration technologies (monitoring and forecasting of transmission and distribution systems, demand-side load management, energy storage, and load balancing with low-carbon fuel substitutes). For each topic, recent historical trends and future prospects are reviewed,more » along with technical challenges, costs, and other issues as appropriate. Although no technology represents an ideal solution, their strengths can be enhanced by deployment in combination, along with grid integration that forms a critical set of enabling technologies to assure a reliable and robust future low-carbon electricity system.« less

The Future of Low-Carbon Electricity

DOE PAGES

Greenblatt, Jeffery B.; Brown, Nicholas R.; Slaybaugh, Rachel; ...

2017-07-10

Here, we review future global demand for electricity and major technologies positioned to supply itwith minimal greenhouse gas (GHG) emissions: renewables (wind, solar, water, geothermal and biomass), nuclear fission, and fossil power with CO 2 capture and sequestration. Two breakthrough technologies (space solar power and nuclear fusion) are discussed as exciting but uncertain additional options for low net GHG emissions (“low-carbon”) electricity generation. Grid integration technologies (monitoring and forecasting of transmission and distribution systems, demand-side load management, energy storage, and load balancing with low-carbon fuel substitutes) are also discussed. For each topic, recent historical trends and future prospects are reviewed,more » along with technical challenges, costs and other issues as appropriate. While no technology represents an ideal solution, their strengths can be enhanced by deployment in combination, along with grid integration that forms a critical set of enabling technologies to assure a reliable and robust future low-carbon electricity system.« less

Characteristics of future air cargo demand and impact on aircraft development - A report on the Cargo/Logistic Airlift Systems Study /CLASS/ project

NASA Technical Reports Server (NTRS)

Whitehead, A. H., Jr.

1978-01-01

The considered study has been conducted to evaluate the future potential for an advanced air cargo transport. A current operations analysis is discussed, taking into account the traffic structure, modal cost comparisons, terminal operations, containerization, and institutional factors. Attention is also given to case studies, a demand forecast, and an advanced air cargo systems analysis. The effects of potential improvements on reducing costs are shown. Improvement to the current infrastructure can occur from 1978 to 1985 with off-the-shelf technology, which when combined with higher load factors for aircraft and containers, can provide up to a 16 percent reduction in total operating costs and a 15 percent rate reduction. The results of the analysis indicate that the proposed changes in the infrastructure and improved cargo loading efficiencies are as important to improving the airlines' financial posture as is the anticipated large dedicated cargo aircraft.

Integrating Solar PV in Utility System Operations: Analytical Framework and Arizona Case Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Jing; Botterud, Audun; Mills, Andrew

2015-06-01

A systematic framework is proposed to estimate the impact on operating costs due to uncertainty and variability in renewable resources. The framework quantifies the integration costs associated with subhourly variability and uncertainty as well as day-ahead forecasting errors in solar PV (photovoltaics) power. A case study illustrates how changes in system operations may affect these costs for a utility in the southwestern United States (Arizona Public Service Company). We conduct an extensive sensitivity analysis under different assumptions about balancing reserves, system flexibility, fuel prices, and forecasting errors. We find that high solar PV penetrations may lead to operational challenges, particularlymore » during low-load and high solar periods. Increased system flexibility is essential for minimizing integration costs and maintaining reliability. In a set of sensitivity cases where such flexibility is provided, in part, by flexible operations of nuclear power plants, the estimated integration costs vary between $1.0 and $4.4/MWh-PV for a PV penetration level of 17%. The integration costs are primarily due to higher needs for hour-ahead balancing reserves to address the increased sub-hourly variability and uncertainty in the PV resource. (C) 2015 Elsevier Ltd. All rights reserved.« less

[Forecast of costs of ecodependent cancer treatment for the development of management decisions].

PubMed

Krasovskiy, V O

2014-01-01

The methodical approach for probabilistic forecasting and differentiation of treatment of costs of ecodependent cancer cases has been elaborated. The modality is useful in the organization of medical aid to cancer patients, in developing management decisions for the reduction the occupational load on the population, as well as in solutions problems in compensation to the population economic and social loss from industrial plants.

Numerical study of Asian dust transport during the springtime of 2001 simulated with the Chemical Weather Forecasting System (CFORS) model

NASA Astrophysics Data System (ADS)

Uno, Itsushi; Satake, Shinsuke; Carmichael, Gregory R.; Tang, Youhua; Wang, Zifa; Takemura, Toshihiko; Sugimoto, Nobuo; Shimizu, Atsushi; Murayama, Toshiyuki; Cahill, Thomas A.; Cliff, Steven; Uematsu, Mitsuo; Ohta, Sachio; Quinn, Patricia K.; Bates, Timothy S.

2004-10-01

The regional-scale aerosol transport model Chemical Weather Forecasting System (CFORS) is used for analysis of large-scale dust phenomena during the Asian Pacific Regional Characterization Experiment (ACE-Asia) intensive observation. Dust modeling results are examined with the surface weather reports, satellite-derived dust index (Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI)), Mie-scattering lidar observation, and surface aerosol observations. The CFORS dust results are shown to accurately reproduce many of the important observed features. Model analysis shows that the simulated dust vertical loading correlates well with TOMS AI and that the dust loading is transported with the meandering of the synoptic-scale temperature field at the 500-hPa level. Quantitative examination of aerosol optical depth shows that model predictions are within 20% difference of the lidar observations for the major dust episodes. The structure of the ACE-Asia Perfect Dust Storm, which occurred in early April, is clarified with the help of the CFORS model analysis. This storm consisted of two boundary layer components and one elevated dust (>6-km height) feature (resulting from the movement of two large low-pressure systems). Time variation of the CFORS dust fields shows the correct onset timing of the elevated dust for each observation site, but the model results tend to overpredict dust concentrations at lower latitude sites. The horizontal transport flux at 130°E longitude is examined, and the overall dust transport flux at 130°E during March-April is evaluated to be 55 Tg.

Wild Fire Emissions for the NOAA Operational HYSPLIT Smoke Model

NASA Astrophysics Data System (ADS)

Huang, H. C.; ONeill, S. M.; Ruminski, M.; Shafran, P.; McQueen, J.; DiMego, G.; Kondragunta, S.; Gorline, J.; Huang, J. P.; Stunder, B.; Stein, A. F.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2015-12-01

Particulate Matter (PM) generated from forest fires often lead to degraded visibility and unhealthy air quality in nearby and downstream areas. To provide near-real time PM information to the state and local agencies, the NOAA/National Weather Service (NWS) operational HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) smoke modeling system (NWS/HYSPLIT smoke) provides the forecast of smoke concentration resulting from fire emissions driven by the NWS North American Model 12 km weather predictions. The NWS/HYSPLIT smoke incorporates the U.S. Forest Service BlueSky Smoke Modeling Framework (BlueSky) to provide smoke fire emissions along with the input fire locations from the NOAA National Environmental Satellite, Data, and Information Service (NESDIS)'s Hazard Mapping System fire and smoke detection system. Experienced analysts inspect satellite imagery from multiple sensors onboard geostationary and orbital satellites to identify the location, size and duration of smoke emissions for the model. NWS/HYSPLIT smoke is being updated to use a newer version of USFS BlueSky. The updated BlueSky incorporates the Fuel Characteristic Classification System version 2 (FCCS2) over the continental U.S. and Alaska. FCCS2 includes a more detailed description of fuel loadings with additional plant type categories. The updated BlueSky also utilizes an improved fuel consumption model and fire emission production system. For the period of August 2014 and June 2015, NWS/HYSPLIT smoke simulations show that fire smoke emissions with updated BlueSky are stronger than the current operational BlueSky in the Northwest U.S. For the same comparisons, weaker fire smoke emissions from the updated BlueSky were observed over the middle and eastern part of the U.S. A statistical evaluation of NWS/HYSPLIT smoke predicted total column concentration compared to NOAA NESDIS GOES EAST Aerosol Smoke Product retrievals is underway. Preliminary results show that using the newer version of BlueSky leads to improved performance of NWS/HYSPLIT-smoke for June 2015. These results are partially due to the default fuel loading selected for Canadian fires that lead to stronger fire emissions there. The use of more realistic Canadian fuel loading may improve NWS/HYSPLIT smoke forecast.

Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

Application of Hybrid Optimization-Expert System for Optimal Power Management on Board Space Power Station

NASA Technical Reports Server (NTRS)

Momoh, James; Chattopadhyay, Deb; Basheer, Omar Ali AL

1996-01-01

The space power system has two sources of energy: photo-voltaic blankets and batteries. The optimal power management problem on-board has two broad operations: off-line power scheduling to determine the load allocation schedule of the next several hours based on the forecast of load and solar power availability. The nature of this study puts less emphasis on speed requirement for computation and more importance on the optimality of the solution. The second category problem, on-line power rescheduling, is needed in the event of occurrence of a contingency to optimally reschedule the loads to minimize the 'unused' or 'wasted' energy while keeping the priority on certain type of load and minimum disturbance of the original optimal schedule determined in the first-stage off-line study. The computational performance of the on-line 'rescheduler' is an important criterion and plays a critical role in the selection of the appropriate tool. The Howard University Center for Energy Systems and Control has developed a hybrid optimization-expert systems based power management program. The pre-scheduler has been developed using a non-linear multi-objective optimization technique called the Outer Approximation method and implemented using the General Algebraic Modeling System (GAMS). The optimization model has the capability of dealing with multiple conflicting objectives viz. maximizing energy utilization, minimizing the variation of load over a day, etc. and incorporates several complex interaction between the loads in a space system. The rescheduling is performed using an expert system developed in PROLOG which utilizes a rule-base for reallocation of the loads in an emergency condition viz. shortage of power due to solar array failure, increase of base load, addition of new activity, repetition of old activity etc. Both the modules handle decision making on battery charging and discharging and allocation of loads over a time-horizon of a day divided into intervals of 10 minutes. The models have been extensively tested using a case study for the Space Station Freedom and the results for the case study will be presented. Several future enhancements of the pre-scheduler and the 'rescheduler' have been outlined which include graphic analyzer for the on-line module, incorporating probabilistic considerations, including spatial location of the loads and the connectivity using a direct current (DC) load flow model.

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Analysis of the electricity demand of Greece for optimal planning of a large-scale hybrid renewable energy system

NASA Astrophysics Data System (ADS)

Tyralis, Hristos; Karakatsanis, Georgios; Tzouka, Katerina; Mamassis, Nikos

2015-04-01

The Greek electricity system is examined for the period 2002-2014. The demand load data are analysed at various time scales (hourly, daily, seasonal and annual) and they are related to the mean daily temperature and the gross domestic product (GDP) of Greece for the same time period. The prediction of energy demand, a product of the Greek Independent Power Transmission Operator, is also compared with the demand load. Interesting results about the change of the electricity demand scheme after the year 2010 are derived. This change is related to the decrease of the GDP, during the period 2010-2014. The results of the analysis will be used in the development of an energy forecasting system which will be a part of a framework for optimal planning of a large-scale hybrid renewable energy system in which hydropower plays the dominant role. Acknowledgement: This research was funded by the Greek General Secretariat for Research and Technology through the research project Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO; grant number 5145)

How to Integrate Variable Power Source into a Power Grid

NASA Astrophysics Data System (ADS)

Asano, Hiroshi

This paper discusses how to integrate variable power source such as wind power and photovoltaic generation into a power grid. The intermittent renewable generation is expected to penetrate for less carbon intensive power supply system, but it causes voltage control problem in the distribution system, and supply-demand imbalance problem in a whole power system. Cooperative control of customers' energy storage equipment such as water heater with storage tank for reducing inverse power flow from the roof-top PV system, the operation technique using a battery system and the solar radiation forecast for stabilizing output of variable generation, smart charging of plug-in hybrid electric vehicles for load frequency control (LFC), and other methods to integrate variable power source with improving social benefits are surveyed.

A study for systematic errors of the GLA forecast model in tropical regions

NASA Technical Reports Server (NTRS)

Chen, Tsing-Chang; Baker, Wayman E.; Pfaendtner, James; Corrigan, Martin

1988-01-01

From the sensitivity studies performed with the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system, it was revealed that the forecast errors in the tropics affect the ability to forecast midlatitude weather in some cases. Apparently, the forecast errors occurring in the tropics can propagate to midlatitudes. Therefore, the systematic error analysis of the GLA forecast system becomes a necessary step in improving the model's forecast performance. The major effort of this study is to examine the possible impact of the hydrological-cycle forecast error on dynamical fields in the GLA forecast system.

GloFAS-Seasonal: Operational Seasonal Ensemble River Flow Forecasts at the Global Scale

NASA Astrophysics Data System (ADS)

Emerton, Rebecca; Zsoter, Ervin; Smith, Paul; Salamon, Peter

2017-04-01

Seasonal hydrological forecasting has potential benefits for many sectors, including agriculture, water resources management and humanitarian aid. At present, no global scale seasonal hydrological forecasting system exists operationally; although smaller scale systems have begun to emerge around the globe over the past decade, a system providing consistent global scale seasonal forecasts would be of great benefit in regions where no other forecasting system exists, and to organisations operating at the global scale, such as disaster relief. We present here a new operational global ensemble seasonal hydrological forecast, currently under development at ECMWF as part of the Global Flood Awareness System (GloFAS). The proposed system, which builds upon the current version of GloFAS, takes the long-range forecasts from the ECMWF System4 ensemble seasonal forecast system (which incorporates the HTESSEL land surface scheme) and uses this runoff as input to the Lisflood routing model, producing a seasonal river flow forecast out to 4 months lead time, for the global river network. The seasonal forecasts will be evaluated using the global river discharge reanalysis, and observations where available, to determine the potential value of the forecasts across the globe. The seasonal forecasts will be presented as a new layer in the GloFAS interface, which will provide a global map of river catchments, indicating whether the catchment-averaged discharge forecast is showing abnormally high or low flows during the 4-month lead time. Each catchment will display the corresponding forecast as an ensemble hydrograph of the weekly-averaged discharge forecast out to 4 months, with percentile thresholds shown for comparison with the discharge climatology. The forecast visualisation is based on a combination of the current medium-range GloFAS forecasts and the operational EFAS (European Flood Awareness System) seasonal outlook, and aims to effectively communicate the nature of a seasonal outlook while providing useful information to users and partners. We demonstrate the first version of an operational GloFAS seasonal outlook, outlining the model set-up and presenting a first look at the seasonal forecasts that will be displayed in the GloFAS interface, and discuss the initial results of the forecast evaluation.

Weather forecasting expert system study

NASA Technical Reports Server (NTRS)

1985-01-01

Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.

Technology development of the Space Transportation System mission and terrestrial applications of satellite technology

NASA Technical Reports Server (NTRS)

1981-01-01

The Space Transportation System (STS) is discussed, including the launch processing system, the thermal protection subsystem, meteorological research, sound supression water system, rotating service structure, improved hypergol or removal systems, fiber optics research, precision positioning, remote controlled solid rocket booster nozzle plugs, ground operations for Centaur orbital transfer vehicle, parachute drying, STS hazardous waste disposal and recycle, toxic waste technology and control concepts, fast analytical densitometry study, shuttle inventory management system, operational intercommunications system improvement, and protective garment ensemble. Terrestrial applications are also covered, including LANDSAT applications to water resources, satellite freeze forecast system, application of ground penetrating radar to soil survey, turtle tracking, evaluating computer drawn ground cover maps, sparkless load pulsar, and coupling a microcomputer and computing integrator with a gas chromatograph.

National Centers for Environmental Prediction

Science.gov Websites

SYSTEM CFS CLIMATE FORECAST SYSTEM NAQFC NAQFC MODEL GEFS GLOBAL ENSEMBLE FORECAST SYSTEM HWRF HURRICANE WEATHER RESEARCH and FORECASTING HMON HMON - OPERATIONAL HURRICANE FORECASTING WAVEWATCH III WAVEWATCH III

Forecasting the climate response to volcanic eruptions: prediction skill related to stratospheric aerosol forcing

NASA Astrophysics Data System (ADS)

Ménégoz, M.; Bilbao, R.; Bellprat, O.; Guemas, V.; Doblas-Reyes, F. J.

2018-06-01

The last major volcanic eruptions, the Agung in 1963, El Chichon in 1982 and Pinatubo in 1991, were each associated with a cooling of the troposphere that has been observed over large continental areas and over the Western Pacific, the Indian Ocean and the Southern Atlantic. Simultaneously, Eastern tropical Pacific temperatures increased due to prevailing El Niño conditions. Here we show that the pattern of these near-surface temperature anomalies is partly reproduced with decadal simulations of the EC-Earth model initialised with climate observations and forced with an estimate of the observed volcanic aerosol optical thickness. Sensitivity experiments highlight a cooling induced by the volcanic forcing, whereas El Niño events following the eruptions would have occurred even without volcanic eruptions. Focusing on the period 1961–2001, the main source of skill of this decadal forecast system during the first 2 years is related to the initialisation of the model. The contribution of the initialisation to the skill becomes smaller than the contribution of the volcanic forcing after two years, the latter being substantial in the Western Pacific, the Indian Ocean and the Western Atlantic. Two simple protocols for real time forecasts are investigated: using the forcing of a past volcanic eruption to simulate the forcing of a new one, and applying a two-year exponential decay to the initial stratospheric aerosol load observed at the beginning of the forecast. This second protocol applied in retrospective forecasts allows a partial reproduction of the skill attained with observed forcing.

Support vector machine for day ahead electricity price forecasting

NASA Astrophysics Data System (ADS)

Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

2015-05-01

Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

Streamlining On-Demand Access to Joint Polar Satellite System (JPSS) Data Products for Weather Forecasting

NASA Astrophysics Data System (ADS)

Evans, J. D.; Tislin, D.

2017-12-01

Observations from the Joint Polar Satellite System (JPSS) support National Weather Service (NWS) forecasters, whose Advanced Weather Interactive Processing System (AWIPS) Data Delivery (DD) will access JPSS data products on demand from the National Environmental Satellite, Data, and Information Service (NESDIS) Product Distribution and Access (PDA) service. Based on the Open Geospatial Consortium (OGC) Web Coverage Service, this on-demand service promises broad interoperability and frugal use of data networks by serving only the data that a user needs. But the volume, velocity, and variety of JPSS data products impose several challenges to such a service. It must be efficient to handle large volumes of complex, frequently updated data, and to fulfill many concurrent requests. It must offer flexible data handling and delivery, to work with a diverse and changing collection of data, and to tailor its outputs into products that users need, with minimal coordination between provider and user communities. It must support 24x7 operation, with no pauses in incoming data or user demand; and it must scale to rapid changes in data volume, variety, and demand as new satellites launch, more products come online, and users rely increasingly on the service. We are addressing these challenges in order to build an efficient and effective on-demand JPSS data service. For example, on-demand subsetting by many users at once may overload a server's processing capacity or its disk bandwidth - unless alleviated by spatial indexing, geolocation transforms, or pre-tiling and caching. Filtering by variable (/ band / layer) may also alleviate network loads, and provide fine-grained variable selection; to that end we are investigating how best to provide random access into the variety of spatiotemporal JPSS data products. Finally, producing tailored products (derivatives, aggregations) can boost flexibility for end users; but some tailoring operations may impose significant server loads. Operating this service in a cloud computing environment allows cost-effective scaling during the development and early deployment phases - and perhaps beyond. We will discuss how NESDIS and NWS are assessing and addressing these challenges to provide timely and effective access to JPSS data products for weather forecasters throughout the country.

STS-121: Discovery L-1 Countdown Status Briefing

NASA Technical Reports Server (NTRS)

2006-01-01

Bruce Buckingham, NASA Public Affairs, introduces Jeff Spaulding, NASA Test Director; Debbie Hahn, STS-121 Payload Manager; and Kathy Winters, Shuttle Weather Officer. Spaulding gives his opening statement on this one day prior to the launching of the Space Shuttle Discovery. He discusses the following topics: 1) Launch of the Space Shuttle Discovery; 2) Weather; 3) Load over of onboard reactants; 4) Hold time for liquid hydrogen; 5) Stowage of Mid-deck completion; 6) Check-out of onboard and ground network systems; 7) Launch windows; 8) Mission duration; 9) Extravehicular (EVA) plans; 10) Space Shuttle landing day; and 11) Scrub turn-around plans. Hahn presents and discusses a short video of the STS-121 payload flow. Kathy Winters gives her weather forecast for launch. She then presents a slide presentation on the following weather conditions for the Space Shuttle Discovery: 1) STS-121 Tanking Forecast; 2) Launch Forecast; 3) SRB Recovery; 4) CONUS Launch; 5) TAL Launch; 6) 24 Hour Delay; 7) CONUS 24 Hour; 8) TAL 24 Hour; 9) 48 Hour Launch; 10) CONUS 48 Hour; and 11) TAL 48 Hour. The briefing ends with a question and answer period from the media.

David Palchak | NREL

Science.gov Websites

Electrical load forecasting with artificial neural networks Demand-side management optimization with Matlab -58491. D. Palchak, S. Suryanarayanan, and D. Zimmerle. "An Artificial Neural Network in Short-Term

An Early-Warning System for Volcanic Ash Dispersal: The MAFALDA Procedure

NASA Astrophysics Data System (ADS)

Barsotti, S.; Nannipieri, L.; Neri, A.

2006-12-01

Forecasts of the dispersal of volcanic ash is a fundamental goal in order to mitigate its potential impact on urbanized areas and transport routes surrounding explosive volcanoes. To this aim we developed an early- warning procedure named MAFALDA (Modeling And Forecasting Ash Loading and Dispersal in the Atmosphere). Such tool is able to quantitatively forecast the atmospheric concentration of ash as well as the ground deposition as a function of time over a 3D spatial domain.\\The main features of MAFALDA are: (1) the use of the hybrid Lagrangian-Eulerian code VOL-CALPUFF able to describe both the rising column phase and the atmospheric dispersal as a function of weather conditions, (2) the use of high-resolution weather forecasting data, (3) the short execution time that allows to analyse a set of scenarios and (4) the web-based CGI software application (written in Perl programming language) that shows the results in a standard graphical web interface and makes it suitable as an early-warning system during volcanic crises.\\MAFALDA is composed by a computational part that simulates the ash cloud dynamics and a graphical interface for visualizing the modelling results. The computational part includes the codes for elaborating the meteorological data, the dispersal code and the post-processing programs. These produces hourly 2D maps of aerial ash concentration at several vertical levels, extension of "threat" area on air and 2D maps of ash deposit on the ground, in addition to graphs of hourly variations of column height.\\The processed results are available on the web by the graphical interface and the users can choose, by drop-down menu, which data to visualize. \\A first partial application of the procedure has been carried out for Mt. Etna (Italy). In this case, the procedure simulates four volcanological scenarios characterized by different plume intensities and uses 48-hrs weather forecasting data with a resolution of 7 km provided by the Italian Air Force.

Evolution of damage during deformation in porous granular materials (Louis Néel Medal Lecture)

NASA Astrophysics Data System (ADS)

Main, Ian

2014-05-01

'Crackling noise' occurs in a wide variety of systems that respond to external forcing in an intermittent way, leading to sudden bursts of energy release similar to those heard when crunching up a piece of paper or listening to a fire. In mineral magnetism ('Barkhausen') crackling noise occurs due to sudden changes in the size and orientation of microscopic ferromagnetic domains when the external magnetic field is changed. In rock physics sudden changes in internal stress associated with microscopically brittle failure events lead to acoustic emissions that can be recorded on the sample boundary, and used to infer the state of internal damage. Crackling noise is inherently stochastic, but the population of events often exhibits remarkably robust scaling properties, in terms of the source area, duration, energy, and in the waiting time between events. Here I describe how these scaling properties emerge and evolve spontaneously in a fully-dynamic discrete element model of sedimentary rocks subject to uniaxial compression at a constant strain rate. The discrete elements have structural disorder similar to that of a real rock, and this is the only source of heterogeneity. Despite the stationary loading and the lack of any time-dependent weakening processes, the results are all characterized by emergent power law distributions over a broad range of scales, in agreement with experimental observation. As deformation evolves, the scaling exponents change systematically in a way that is similar to the evolution of damage in experiments on real sedimentary rocks. The potential for real-time failure forecasting is examined by using synthetic and real data from laboratory tests and prior to volcanic eruptions. The combination of non-linearity and an irreducible stochastic component leads to significant variations in the precision and accuracy of the forecast failure time, leading to a significant proportion of 'false alarms' (forecast too early) and 'missed events' (forecast too late), as well as an over-optimistic assessments of forecasting power and quality when the failure time is known (the 'benefit of hindsight'). The evolution becomes progressively more complex, and the forecasting power diminishes, in going from ideal synthetics to controlled laboratory tests to open natural systems at larger scales in space and time.

Forecasting giant, catastrophic slope collapse: lessons from Vajont, Northern Italy

NASA Astrophysics Data System (ADS)

Kilburn, Christopher R. J.; Petley, David N.

2003-08-01

Rapid, giant landslides, or sturzstroms, are among the most powerful natural hazards on Earth. They have minimum volumes of ˜10 6-10 7 m 3 and, normally preceded by prolonged intervals of accelerating creep, are produced by catastrophic and deep-seated slope collapse (loads ˜1-10 MPa). Conventional analyses attribute rapid collapse to unusual mechanisms, such as the vaporization of ground water during sliding. Here, catastrophic collapse is related to self-accelerating rock fracture, common in crustal rocks at loads ˜1-10 MPa and readily catalysed by circulating fluids. Fracturing produces an abrupt drop in resisting stress. Measured stress drops in crustal rock account for minimum sturzstrom volumes and rapid collapse accelerations. Fracturing also provides a physical basis for quantitatively forecasting catastrophic slope failure.

A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

Effects of recent energy system changes on CO2 projections for the United States.

PubMed

Lenox, Carol S; Loughlin, Daniel H

2017-09-21

Recent projections of future United States carbon dioxide (CO 2 ) emissions are considerably lower than projections made just a decade ago. A myriad of factors have contributed to lower forecasts, including reductions in end-use energy service demands, improvements in energy efficiency, and technological innovations. Policies that have encouraged these changes include renewable portfolio standards, corporate vehicle efficiency standards, smart growth initiatives, revisions to building codes, and air and climate regulations. Understanding the effects of these and other factors can be advantageous as society evaluates opportunities for achieving additional CO 2 reductions. Energy system models provide a means to develop such insights. In this analysis, the MARKet ALlocation (MARKAL) model was applied to estimate the relative effects of various energy system changes that have happened since the year 2005 on CO 2 projections for the year 2025. The results indicate that transformations in the transportation and buildings sectors have played major roles in lowering projections. Particularly influential changes include improved vehicle efficiencies, reductions in projected travel demand, reductions in miscellaneous commercial electricity loads, and higher efficiency lighting. Electric sector changes have also contributed significantly to the lowered forecasts, driven by demand reductions, renewable portfolio standards, and air quality regulations.

Recommendations for a wind profiling network to support Space Shuttle launches

NASA Technical Reports Server (NTRS)

Zamora, R. J.

1992-01-01

The feasibility is examined of a network of clear air radar wind profilers to forecast wind conditions before Space Shuttle launches during winter. Currently, winds are measured only in the vicinity of the shuttle launch site and wind loads on the launch vehicle are estimated using these measurements. Wind conditions upstream of the Cape are not monitored. Since large changes in the wind shear profile can be associated with weather systems moving over the Cape, it may be possible to improve wind forecasts over the launch site if wind measurements are made upstream. A radar wind profiling system is in use at the Space Shuttle launch site. This system can monitor the wind profile continuously. The existing profiler could be combined with a number of radars located upstream of the launch site. Thus, continuous wind measurements would be available upstream and at the Cape. NASA-Marshall representatives have set the requirements for radar wind profiling network. The minimum vertical resolution of the network must be set so that the wind shears over the depths greater than or = 1 km will be detected. The network should allow scientists and engineers to predict the wind profile over the Cape 6 hours before a Space Shuttle launch.

Investigation of prospects for forecasting non-linear time series by example of drilling oil and gas wells

NASA Astrophysics Data System (ADS)

Vlasenko, A. V.; Sizonenko, A. B.; Zhdanov, A. A.

2018-05-01

Discrete time series or mappings are proposed for describing the dynamics of a nonlinear system. The article considers the problems of forecasting the dynamics of the system from the time series generated by it. In particular, the commercial rate of drilling oil and gas wells can be considered as a series where each next value depends on the previous one. The main parameter here is the technical drilling speed. With the aim of eliminating the measurement error and presenting the commercial speed of the object to the current with a good accuracy, future or any of the elapsed time points, the use of the Kalman filter is suggested. For the transition from a deterministic model to a probabilistic one, the use of ensemble modeling is suggested. Ensemble systems can provide a wide range of visual output, which helps the user to evaluate the measure of confidence in the model. In particular, the availability of information on the estimated calendar duration of the construction of oil and gas wells will allow drilling companies to optimize production planning by rationalizing the approach to loading drilling rigs, which ultimately leads to maximization of profit and an increase of their competitiveness.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.

Characterizing and analyzing ramping events in wind power, solar power, load, and netload

DOE PAGES

Cui, Mingjian; Zhang, Jie; Feng, Cong; ...

2017-04-07

Here, one of the biggest concerns associated with integrating a large amount of renewable energy into the power grid is the ability to handle large ramps in the renewable power output. For the sake of system reliability and economics, it is essential for power system operators to better understand the ramping features of renewable, load, and netload. An optimized swinging door algorithm (OpSDA) is used and extended to accurately and efficiently detect ramping events. For wind power ramps detection, a process of merging 'bumps' (that have a different changing direction) into adjacent ramping segments is included to improve the performancemore » of the OpSDA method. For solar ramps detection, ramping events that occur in both clear-sky and measured (or forecasted) solar power are removed to account for the diurnal pattern of solar generation. Ramping features are extracted and extensively compared between load and netload under different renewable penetration levels (9.77%, 15.85%, and 51.38%). Comparison results show that (i) netload ramp events with shorter durations and smaller magnitudes occur more frequently when renewable penetration level increases, and the total number of ramping events also increases; and (ii) different ramping characteristics are observed in load and netload even with a low renewable penetration level.« less

A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results

NASA Astrophysics Data System (ADS)

Pan, Yujie; Xue, Ming; Zhu, Kefeng; Wang, Mingjun

2018-05-01

A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/˜13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.

Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations

NASA Astrophysics Data System (ADS)

Boichu, Marie; Clarisse, Lieven; Khvorostyanov, Dmitry; Clerbaux, Cathy

2014-04-01

Forecasting the dispersal of volcanic clouds during an eruption is of primary importance, especially for ensuring aviation safety. As volcanic emissions are characterized by rapid variations of emission rate and height, the (generally) high level of uncertainty in the emission parameters represents a critical issue that limits the robustness of volcanic cloud dispersal forecasts. An inverse modeling scheme, combining satellite observations of the volcanic cloud with a regional chemistry-transport model, allows reconstructing this source term at high temporal resolution. We demonstrate here how a progressive assimilation of freshly acquired satellite observations, via such an inverse modeling procedure, allows for delivering robust sulfur dioxide (SO2) cloud dispersal forecasts during the eruption. This approach provides a computationally cheap estimate of the expected location and mass loading of volcanic clouds, including the identification of SO2-rich parts.

Application of Advanced Technologies to Small, Short-haul Air Transports

NASA Technical Reports Server (NTRS)

Adcock, C.; Coverston, C.; Knapton, B.

1980-01-01

A study was conducted of the application of advanced technologies to small, short-haul transport aircraft. A three abreast, 30 passenger design for flights of approximately 100 nautical miles was evaluated. Higher wing loading, active flight control, and a gust alleviation system results in improved ride quality. Substantial savings in fuel and direct operating cost are forecast. An aircraft of this configuration also has significant benefits in forms of reliability and operability which should enable it to sell a total of 450 units through 1990, of which 80% are for airline use.

The Wind Integration National Dataset (WIND) toolkit (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caroline Draxl: NREL

2014-01-01

Regional wind integration studies require detailed wind power output data at many locations to perform simulations of how the power system will operate under high penetration scenarios. The wind datasets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as being time synchronized with available load profiles.As described in this presentation, the WIND Toolkit fulfills these requirements by providing a state-of-the-art national (US) wind resource, power production and forecast dataset.

FAA Aviation Forecasts Fiscal Years 1988-1999.

DTIC Science & Technology

1988-02-01

in the 48 contiguous States, Hawaii, Puerto Rico, and the U.S. Virgin Islands. Excluded fromn the data base is activity in Alaska, other U.S...passengerl miles increased b%-’"’. 17.2 percent. Traffic in Hawaii, Puerto Rico, and the U.S. Virgin Islands, ., Ihio wevr, haid slower growth with passenger...trip length for Hawaii/Puerto Rico/ Virgin Islands is expected to remain constant at 98.0 miles over the forecast period. The average industry load

Operation and Management of Thermostatically Controlled Loads for Providing Regulation Services to Power Grids

NASA Astrophysics Data System (ADS)

Vanouni, Maziar

The notion of demand-side participation in power systems operation and control is on the verge of realization because of the advancement in the required technologies an tools like communications, smart meters, sensor networks, large data management techniques, large scale optimization method, etc. Therefore, demand-response (DR) programs can be one of the prosperous solutions to accommodate part of the increasing demand for load balancing services which is brought about by the high penetration of intermittent renewable energies in power systems. This dissertation studies different aspects of the DR programs that utilized the thermostatically controlled loads (TCLs) to provide load balancing services. The importance of TCLs among the other loads lie on their flexibility in power consumption pattern while the customer/end-user comfort is not (or minimally) impacted. Chapter 2 discussed a previously presented direct load control (DLC) to control the power consumption of aggregated TCLs. The DLC method performs a power tracking control and based on central approach where a central controller broadcasts the control command to the dispersed TCLs to toggle them on/off. The central controller receives measurement feedback from the TCLs once per couple of minutes to run a successful forecast process. The performance evaluation criteria to evaluate the load balancing service provided by the TCLs are presented. The results are discussed under different scenarios and situation. The numerical results show the proper performance of the DLC method. This DLC method is used as the control method in all the studies in this dissertation. Chapter 3 presents performance improvements for the original method in Chapter 2 by communicating two more pieces of information called forecast parameters (FPs). Communicating improves the forecast process in the DLC and hence, both performance accuracy and the amount of tear-and-wear imposed on the TCLs. Chapter 4 formulates a stochastic optimization model for a load aggregator (LA) to participate in the performance-based regulation markets (PBRM). PBRMs are the recently developed and practiced regulation market structure recommended by Federal Energy Regulatory Commission (FERC) in 2011. In PBRMs, regulation resources are paid based on both regulation capacity bids and the regulation performance including the provided mileage and the performance accuracy. In order to develop the income from the PBRM, the convention of California Independent System Operator (CAISO) is used. In the presented optimization model, the amount of tear-and-wear imposed on the TCLs are confined to prevent abrupt switching of TCLs. In Chapter 5, a two-stage reward allocation mechanism is developed for a LA recruiting TCLs for regulation service provision. The mechanism helps the LA to distribute the total reward (earned from regulation service provision) among the TCLs according to their contribution in the whole provided service. In the first stage, TCLs are prioritized based on their service provision capability. In order to do so, an index called SPCI is presented to quantify TCLs capability/flexibility and therefore, prioritize them. After prioritization TCLs a priority list is constructed in the first stage. In the second stage, a reward curve is constructed representing the functionality of the possible total reward with respect to the number top TCLs in the priority list. Then, the allocated reward to individual TCLs is calculated by applying the incremental method on the constructed reward curve. This presented reward allocation mechanism is based on the definition of maximum service capacity (MSC) for a control group including TCLs. MSC is defined and its calculation method is presented before discussing the two stages of the reward allocation mechanism. The numerical results proves the suitability of the proposed prioritization method as it is observed the TCLs with higher rankings can contribute more to the total reward in comparison to the TCLs with lower rankings in the priority list.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.

PubMed

Liu, Da; Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

PubMed Central

Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012. PMID:27281032

Study on power grid characteristics in summer based on Linear regression analysis

NASA Astrophysics Data System (ADS)

Tang, Jin-hui; Liu, You-fei; Liu, Juan; Liu, Qiang; Liu, Zhuan; Xu, Xi

2018-05-01

The correlation analysis of power load and temperature is the precondition and foundation for accurate load prediction, and a great deal of research has been made. This paper constructed the linear correlation model between temperature and power load, then the correlation of fault maintenance work orders with the power load is researched. Data details of Jiangxi province in 2017 summer such as temperature, power load, fault maintenance work orders were adopted in this paper to develop data analysis and mining. Linear regression models established in this paper will promote electricity load growth forecast, fault repair work order review, distribution network operation weakness analysis and other work to further deepen the refinement.

Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe

NASA Astrophysics Data System (ADS)

Meißner, Dennis; Klein, Bastian; Ionita, Monica

2017-12-01

Traditionally, navigation-related forecasts in central Europe cover short- to medium-range lead times linked to the travel times of vessels to pass the main waterway bottlenecks leaving the loading ports. Without doubt, this aspect is still essential for navigational users, but in light of the growing political intention to use the free capacity of the inland waterway transport in Europe, additional lead time supporting strategic decisions is more and more in demand. However, no such predictions offering extended lead times of several weeks up to several months currently exist for considerable parts of the European waterway network. This paper describes the set-up of a monthly to seasonal forecasting system for the German stretches of the international waterways of the Rhine, Danube and Elbe rivers. Two competitive forecast approaches have been implemented: the dynamical set-up forces a hydrological model with post-processed outputs from ECMWF general circulation model System 4, whereas the statistical approach is based on the empirical relationship (teleconnection) of global oceanic, climate and regional hydro-meteorological data with river flows. The performance of both forecast methods is evaluated in relation to the climatological forecast (ensemble of historical streamflow) and the well-known ensemble streamflow prediction approach (ESP, ensemble based on historical meteorology) using common performance indicators (correlation coefficient; mean absolute error, skill score; mean squared error, skill score; and continuous ranked probability, skill score) and an impact-based evaluation quantifying the potential economic gain. The following four key findings result from this study: (1) as former studies for other regions of central Europe indicate, the accuracy and/or skill of the meteorological forcing used has a larger effect than the quality of initial hydrological conditions for relevant stations along the German waterways. (2) Despite the predictive limitations on longer lead times in central Europe, this study reveals the existence of a valuable predictability of streamflow on monthly up to seasonal timescales along the Rhine, upper Danube and Elbe waterways, and the Elbe achieves the highest skill and economic value. (3) The more physically based and the statistical approach are able to improve the predictive skills and economic value compared to climatology and the ESP approach. The specific forecast skill highly depends on the forecast location, the lead time and the season. (4) Currently, the statistical approach seems to be most skilful for the three waterways investigated. The lagged relationship between the monthly and/or seasonal streamflow and the climatic and/or oceanic variables vary between 1 month (e.g. local precipitation, temperature and soil moisture) up to 6 months (e.g. sea surface temperature). Besides focusing on improving the forecast methodology, especially by combining the individual approaches, the focus is on developing useful forecast products on monthly to seasonal timescales for waterway transport and to operationalize the related forecasting service.

Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts

NASA Astrophysics Data System (ADS)

Christensen, Hannah; Moroz, Irene; Palmer, Tim

2015-04-01

Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.

Quantitative impact of aerosols on numerical weather prediction. Part II: Impacts to IR radiance assimilation

NASA Astrophysics Data System (ADS)

Marquis, J. W.; Campbell, J. R.; Oyola, M. I.; Ruston, B. C.; Zhang, J.

2017-12-01

This is part II of a two-part series examining the impacts of aerosol particles on weather forecasts. In this study, the aerosol indirect effects on weather forecasts are explored by examining the temperature and moisture analysis associated with assimilating dust contaminated hyperspectral infrared radiances. The dust induced temperature and moisture biases are quantified for different aerosol vertical distribution and loading scenarios. The overall impacts of dust contamination on temperature and moisture forecasts are quantified over the west coast of Africa, with the assistance of aerosol retrievals from AERONET, MPL, and CALIOP. At last, methods for improving hyperspectral infrared data assimilation in dust contaminated regions are proposed.

Interactive Forecasting with the National Weather Service River Forecast System

NASA Technical Reports Server (NTRS)

Smith, George F.; Page, Donna

1993-01-01

The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

Proposed Use of the NASA Ames Nebula Cloud Computing Platform for Numerical Weather Prediction and the Distribution of High Resolution Satellite Imagery

NASA Technical Reports Server (NTRS)

Limaye, Ashutosh S.; Molthan, Andrew L.; Srikishen, Jayanthi

2010-01-01

The development of the Nebula Cloud Computing Platform at NASA Ames Research Center provides an open-source solution for the deployment of scalable computing and storage capabilities relevant to the execution of real-time weather forecasts and the distribution of high resolution satellite data to the operational weather community. Two projects at Marshall Space Flight Center may benefit from use of the Nebula system. The NASA Short-term Prediction Research and Transition (SPoRT) Center facilitates the use of unique NASA satellite data and research capabilities in the operational weather community by providing datasets relevant to numerical weather prediction, and satellite data sets useful in weather analysis. SERVIR provides satellite data products for decision support, emphasizing environmental threats such as wildfires, floods, landslides, and other hazards, with interests in numerical weather prediction in support of disaster response. The Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS) has been configured for Nebula cloud computing use via the creation of a disk image and deployment of repeated instances. Given the available infrastructure within Nebula and the "infrastructure as a service" concept, the system appears well-suited for the rapid deployment of additional forecast models over different domains, in response to real-time research applications or disaster response. Future investigations into Nebula capabilities will focus on the development of a web mapping server and load balancing configuration to support the distribution of high resolution satellite data sets to users within the National Weather Service and international partners of SERVIR.

Performance of an Advanced MOS System in the 1996-97 National Collegiate Weather Forecasting Contest.

NASA Astrophysics Data System (ADS)

Vislocky, Robert L.; Fritsch, J. Michael

1997-12-01

A prototype advanced model output statistics (MOS) forecast system that was entered in the 1996-97 National Collegiate Weather Forecast Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype system uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS forecasts, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the forecast site. The forecasts are totally objective and can be generated quickly on a personal computer. Other "objective" forms of guidance tracked in the contest are 1) the consensus forecast (i.e., the average of the forecasts from all of the human participants), 2) the combination of NGM raw output (for precipitation forecasts) and NGM MOS guidance (for temperature forecasts), and 3) the combination of Eta Model raw output (for precipitation forecasts) and AVN MOS guidance (for temperature forecasts).Results show that the advanced MOS system finished in 20th place out of 737 original entrants, or better than approximately 97% of the human forecasters who entered the contest. Moreover, the advanced MOS system was slightly better than consensus (23d place). The fact that an objective forecast system finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable "opponent" in forecast competitions. Equally significant is that the advanced MOS system was superior to the traditional guidance products available from the National Centers for Environmental Prediction (NCEP). Specifically, the combination of NGM raw output and NGM MOS guidance finished in 175th place, and the combination of Eta Model raw output and AVN MOS guidance finished in 266th place. The latter result is most intriguing since the proposed elimination of all NGM products would likely result in a serious degradation of objective products disseminated by NCEP, unless they are replaced with equal or better substitutes. On the other hand, the positive performance of the prototype advanced MOS system shows that it is possible to create a single objective product that is not only superior to currently available objective guidance products, but is also on par with some of the better human forecasters.

Using JPSS VIIRS Fire Radiative Power Data to Forecast Biomass Burning Emissions and Smoke Transport by the High Resolution Rapid Refresh Model

NASA Astrophysics Data System (ADS)

Ahmadov, R.; Grell, G. A.; James, E.; Alexander, C.; Stewart, J.; Benjamin, S.; McKeen, S. A.; Csiszar, I. A.; Tsidulko, M.; Pierce, R. B.; Pereira, G.; Freitas, S. R.; Goldberg, M.

2017-12-01

We present a new real-time smoke modeling system, the High Resolution Rapid Refresh coupled with smoke (HRRR-Smoke), to simulate biomass burning (BB) emissions, plume rise and smoke transport in real time. The HRRR is the NOAA Earth System Research Laboratory's 3km grid spacing version of the Weather Research and Forecasting (WRF) model used for weather forecasting. Here we make use of WRF-Chem (the WRF model coupled with chemistry) and simulate fine particulate matter (smoke) emissions emitted by BB. The HRRR-Smoke modeling system ingests fire radiative power (FRP) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (S-NPP) satellite to calculate BB emissions. The FRP product is based on processing 750m resolution "M" bands. The algorithms for fire detection and FRP retrieval are consistent with those used to generate the MODIS fire detection data. For the purpose of ingesting VIIRS fire data into the HRRR-Smoke model, text files are generated to provide the location and detection confidence of fire pixels, as well as FRP. The VIIRS FRP data from the text files are processed and remapped over the HRRR-Smoke model domains. We process the FRP data to calculate BB emissions (smoldering part) and fire size for the model input. In addition, HRRR-Smoke uses the FRP data to simulate the injection height for the flaming emissions using concurrently simulated meteorological fields by the model. Currently, there are two 3km resolution domains covering the contiguous US and Alaska which are used to simulate smoke in real time. In our presentation, we focus on the CONUS domain. HRRR-Smoke is initialized 4 times per day to forecast smoke concentrations for the next 36 hours. The VIIRS FRP data, as well as near-surface and vertically integrated smoke mass concentrations are visualized for every forecast hour. These plots are provided to the public via the HRRR-Smoke web-page: https://rapidrefresh.noaa.gov/HRRRsmoke/. Model evaluations for a case study are presented, where simulated smoke concentrations are compared with hourly PM2.5 measurements from EPA's Air Quality System network. These comparisons demonstrate the model's ability in simulating high aerosol loadings during major wildfire events in the western US.

Intelligent demand side management of residential building energy systems

NASA Astrophysics Data System (ADS)

Sinha, Maruti N.

Advent of modern sensing technologies, data processing capabilities and rising cost of energy are driving the implementation of intelligent systems in buildings and houses which constitute 41% of total energy consumption. The primary motivation has been to provide a framework for demand-side management and to improve overall reliability. The entire formulation is to be implemented on NILM (Non-Intrusive Load Monitoring System), a smart meter. This is going to play a vital role in the future of demand side management. Utilities have started deploying smart meters throughout the world which will essentially help to establish communication between utility and consumers. This research is focused on investigation of a suitable thermal model of residential house, building up control system and developing diagnostic and energy usage forecast tool. The present work has considered measurement based approach to pursue. Identification of building thermal parameters is the very first step towards developing performance measurement and controls. The proposed identification technique is PEM (Prediction Error Method) based, discrete state-space model. The two different models have been devised. First model is focused toward energy usage forecast and diagnostics. Here one of the novel idea has been investigated which takes integral of thermal capacity to identify thermal model of house. The purpose of second identification is to build up a model for control strategy. The controller should be able to take into account the weather forecast information, deal with the operating point constraints and at the same time minimize the energy consumption. To design an optimal controller, MPC (Model Predictive Control) scheme has been implemented instead of present thermostatic/hysteretic control. This is a receding horizon approach. Capability of the proposed schemes has also been investigated.

Using WRF-Urban to Assess Summertime Air Conditioning Electric Loads and Their Impacts on Urban Weather in Beijing

NASA Astrophysics Data System (ADS)

Xu, Xiaoyu; Chen, Fei; Shen, Shuanghe; Miao, Shiguang; Barlage, Michael; Guo, Wenli; Mahalov, Alex

2018-03-01

The air conditioning (AC) electric loads and their impacts on local weather over Beijing during a 5 day heat wave event in 2010 are investigated by using the Weather Research and Forecasting (WRF) model, in which the Noah land surface model with multiparameterization options (Noah-MP) is coupled to the multilayer Building Effect Parameterization and Building Energy Model (BEP+BEM). Compared to the legacy Noah scheme coupled to BEP+BEM, this modeling system shows a better performance, decreasing the root-mean-square error of 2 m air temperature to 1.9°C for urban stations. The simulated AC electric loads in suburban and rural districts are significantly improved by introducing the urban class-dependent building cooled fraction. Analysis reveals that the observed AC electric loads in each district are characterized by a common double peak at 3 p.m. and at 9 p.m. local standard time, and the incorporation of more realistic AC working schedules helps reproduce the evening peak. Waste heat from AC systems has a smaller effect ( 1°C) on the afternoon 2 m air temperature than the evening one (1.5 2.4°C) if AC systems work for 24 h and vent sensible waste heat into air. Influences of AC systems can only reach up to 400 m above the ground for the evening air temperature and humidity due to a shallower urban boundary layer than daytime. Spatially varying maps of AC working schedules and the ratio of sensible to latent waste heat release are critical for correctly simulating the cooling electric loads and capturing the thermal stratification of urban boundary layer.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Mingjian; Zhang, Jie; Feng, Cong

Here, one of the biggest concerns associated with integrating a large amount of renewable energy into the power grid is the ability to handle large ramps in the renewable power output. For the sake of system reliability and economics, it is essential for power system operators to better understand the ramping features of renewable, load, and netload. An optimized swinging door algorithm (OpSDA) is used and extended to accurately and efficiently detect ramping events. For wind power ramps detection, a process of merging 'bumps' (that have a different changing direction) into adjacent ramping segments is included to improve the performancemore » of the OpSDA method. For solar ramps detection, ramping events that occur in both clear-sky and measured (or forecasted) solar power are removed to account for the diurnal pattern of solar generation. Ramping features are extracted and extensively compared between load and netload under different renewable penetration levels (9.77%, 15.85%, and 51.38%). Comparison results show that (i) netload ramp events with shorter durations and smaller magnitudes occur more frequently when renewable penetration level increases, and the total number of ramping events also increases; and (ii) different ramping characteristics are observed in load and netload even with a low renewable penetration level.« less

Volcanic Eruption Forecasts From Accelerating Rates of Drumbeat Long-Period Earthquakes

NASA Astrophysics Data System (ADS)

Bell, Andrew F.; Naylor, Mark; Hernandez, Stephen; Main, Ian G.; Gaunt, H. Elizabeth; Mothes, Patricia; Ruiz, Mario

2018-02-01

Accelerating rates of quasiperiodic "drumbeat" long-period earthquakes (LPs) are commonly reported before eruptions at andesite and dacite volcanoes, and promise insights into the nature of fundamental preeruptive processes and improved eruption forecasts. Here we apply a new Bayesian Markov chain Monte Carlo gamma point process methodology to investigate an exceptionally well-developed sequence of drumbeat LPs preceding a recent large vulcanian explosion at Tungurahua volcano, Ecuador. For more than 24 hr, LP rates increased according to the inverse power law trend predicted by material failure theory, and with a retrospectively forecast failure time that agrees with the eruption onset within error. LPs resulted from repeated activation of a single characteristic source driven by accelerating loading, rather than a distributed failure process, showing that similar precursory trends can emerge from quite different underlying physics. Nevertheless, such sequences have clear potential for improving forecasts of eruptions at Tungurahua and analogous volcanoes.

7 CFR 1710.210 - Waiver of requirements or approval criteria.

Code of Federal Regulations, 2010 CFR

2010-01-01

... SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load Forecasts § 1710.210 Waiver of requirements or approval criteria. For good cause...

7 CFR 1710.210 - Waiver of requirements or approval criteria.

Code of Federal Regulations, 2011 CFR

2011-01-01

... SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load Forecasts § 1710.210 Waiver of requirements or approval criteria. For good cause...

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less

Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

2017-04-01

Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the reliability diagram. This study covers 10 nordic watersheds. We show that forecast performance according to the CRPS varies with lead-time but also with the period of the year. The raw forecasts from the ECMWF System4 display important biases for both temperature and precipitation, which need to be corrected. The linear scaling method is used for this purpose and is found effective. Bias correction improves forecasts performance, especially during the summer when the precipitations are over-estimated. According to the CRPS, bias corrected forecasts from System4 show performances comparable to those of the ESP system. However, the Ignorance score, which penalizes the lack of calibration (under-dispersive forecasts in this case) more severely than the CRPS, provides a different outlook for the comparison of the two systems. In fact, according to the Ignorance score, the ESP system outperforms forecasts based on System4 in most cases. This illustrates that the joint use of several metrics is crucial to assess the quality of a forecasts system thoroughly. Globally, ESP provide reliable forecasts which can be over-dispersed whereas bias corrected ECMWF System4 forecasts are sharper but at the risk of missing events.

Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

2011-12-01

Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by observed atmospheric forcing. The forecast skills from the dynamical seasonal models (CFSv1, CFSv2, EUROSIP) and CPC are also compared with forecasts based on the Ensemble Streamflow Prediction (ESP) method, which uses initial conditions and historical forcings to generate seasonal forecasts. The skill of the system to predict drought, drought recovery and related hydrological conditions such as low-flows is assessed, along with quantified uncertainty.

Verification of Ensemble Forecasts for the New York City Operations Support Tool

NASA Astrophysics Data System (ADS)

Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

2012-12-01

The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble forecasts is needed to verify that the post-processed forecasts are unbiased, statistically reliable, and preserve the skill inherent in the "raw" NWS ensemble forecasts. A verification procedure and set of metrics will be presented that provide an objective assessment of ensemble forecasts. The procedure will be applied to both raw ensemble hindcasts and to post-processed ensemble hindcasts. The verification metrics will be used to validate proper functioning of the post-processor and to provide a benchmark for comparison of different types of forecasts. For example, current NWS ensemble forecasts are based on climatology, using each historical year to generate a forecast trace. The NWS Hydrologic Ensemble Forecast System (HEFS) under development will utilize output from both the National Oceanic Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFS). Incorporating short-term meteorological forecasts and longer-term climate forecast information should provide sharper, more accurate forecasts. Hindcasts from HEFS will enable New York City to generate verification results to validate the new forecasts and further fine-tune system operating rules. Project verification results will be presented for different watersheds across a range of seasons, lead times, and flow levels to assess the quality of the current ensemble forecasts.

Recent Trends in Variable Generation Forecasting and Its Value to the Power System

DOE PAGES

Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; ...

2014-12-23

We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value ofmore » adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.« less

Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas; Liniger, Mark

2016-04-01

Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.

Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE PAGES

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara; ...

2017-06-16

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less

Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less

Resolution of Probabilistic Weather Forecasts with Application in Disease Management.

PubMed

Hughes, G; McRoberts, N; Burnett, F J

2017-02-01

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

Online Analysis of Wind and Solar Part II: Transmission Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

2012-01-31

To facilitate wider penetration of renewable resources without compromising system reliability concerns arising from the lack of predictability of intermittent renewable resources, a tool for use by California Independent System Operator (CAISO) power grid operators was developed by Pacific Northwest National Laboratory (PNNL) in conjunction with CAISO with funding from California Energy Commission. The tool analyzes and displays the impacts of uncertainties in forecasts of loads and renewable generation on: (1) congestion, (2)voltage and transient stability margins, and (3)voltage reductions and reactive power margins. The impacts are analyzed in the base case and under user-specified contingencies.A prototype of the toolmore » has been developed and implemented in software.« less

The Texas Children's Hospital immunization forecaster: conceptualization to implementation.

PubMed

Cunningham, Rachel M; Sahni, Leila C; Kerr, G Brady; King, Laura L; Bunker, Nathan A; Boom, Julie A

2014-12-01

Immunization forecasting systems evaluate patient vaccination histories and recommend the dates and vaccines that should be administered. We described the conceptualization, development, implementation, and distribution of a novel immunization forecaster, the Texas Children's Hospital (TCH) Forecaster. In 2007, TCH convened an internal expert team that included a pediatrician, immunization nurse, software engineer, and immunization subject matter experts to develop the TCH Forecaster. Our team developed the design of the model, wrote the software, populated the Excel tables, integrated the software, and tested the Forecaster. We created a table of rules that contained each vaccine's recommendations, minimum ages and intervals, and contraindications, which served as the basis for the TCH Forecaster. We created 15 vaccine tables that incorporated 79 unique dose states and 84 vaccine types to operationalize the entire United States recommended immunization schedule. The TCH Forecaster was implemented throughout the TCH system, the Indian Health Service, and the Virginia Department of Health. The TCH Forecast Tester is currently being used nationally. Immunization forecasting systems might positively affect adherence to vaccine recommendations. Efforts to support health care provider utilization of immunization forecasting systems and to evaluate their impact on patient care are needed.

Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

NASA Astrophysics Data System (ADS)

Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

2017-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of stakeholder-driven applications of the K-PREP system, including empirical forecasts for circumboreal fire activity.

Hybrid Intrusion Forecasting Framework for Early Warning System

NASA Astrophysics Data System (ADS)

Kim, Sehun; Shin, Seong-Jun; Kim, Hyunwoo; Kwon, Ki Hoon; Han, Younggoo

Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.

Stochastic Multi-Timescale Power System Operations With Variable Wind Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Hongyu; Krad, Ibrahim; Florita, Anthony

This paper describes a novel set of stochastic unit commitment and economic dispatch models that consider stochastic loads and variable generation at multiple operational timescales. The stochastic model includes four distinct stages: stochastic day-ahead security-constrained unit commitment (SCUC), stochastic real-time SCUC, stochastic real-time security-constrained economic dispatch (SCED), and deterministic automatic generation control (AGC). These sub-models are integrated together such that they are continually updated with decisions passed from one to another. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies with deterministic approaches are conductedmore » in low wind and high wind penetration scenarios to highlight the advantages of the proposed methodology, one with perfect forecasts and the other with current state-of-the-art but imperfect deterministic forecasts. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and reliability metrics to provide a broader view of its impact.« less

Evaluation and prediction of solar radiation for energy management based on neural networks

NASA Astrophysics Data System (ADS)

Aldoshina, O. V.; Van Tai, Dinh

2017-08-01

Currently, there is a high rate of distribution of renewable energy sources and distributed power generation based on intelligent networks; therefore, meteorological forecasts are particularly useful for planning and managing the energy system in order to increase its overall efficiency and productivity. The application of artificial neural networks (ANN) in the field of photovoltaic energy is presented in this article. Implemented in this study, two periodically repeating dynamic ANS, that are the concentration of the time delay of a neural network (CTDNN) and the non-linear autoregression of a network with exogenous inputs of the NAEI, are used in the development of a model for estimating and daily forecasting of solar radiation. ANN show good productivity, as reliable and accurate models of daily solar radiation are obtained. This allows to successfully predict the photovoltaic output power for this installation. The potential of the proposed method for controlling the energy of the electrical network is shown using the example of the application of the NAEI network for predicting the electric load.

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

NASA Astrophysics Data System (ADS)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

The NRL relocatable ocean/acoustic ensemble forecast system

NASA Astrophysics Data System (ADS)

Rowley, C.; Martin, P.; Cummings, J.; Jacobs, G.; Coelho, E.; Bishop, C.; Hong, X.; Peggion, G.; Fabre, J.

2009-04-01

A globally relocatable regional ocean nowcast/forecast system has been developed to support rapid implementation of new regional forecast domains. The system is in operational use at the Naval Oceanographic Office for a growing number of regional and coastal implementations. The new system is the basis for an ocean acoustic ensemble forecast and adaptive sampling capability. We present an overview of the forecast system and the ocean ensemble and adaptive sampling methods. The forecast system consists of core ocean data analysis and forecast modules, software for domain configuration, surface and boundary condition forcing processing, and job control, and global databases for ocean climatology, bathymetry, tides, and river locations and transports. The analysis component is the Navy Coupled Ocean Data Assimilation (NCODA) system, a 3D multivariate optimum interpolation system that produces simultaneous analyses of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. The forecast component is the Navy Coastal Ocean Model (NCOM). The system supports one-way nesting and multiple assimilation methods. The ensemble system uses the ensemble transform technique with error variance estimates from the NCODA analysis to represent initial condition error. Perturbed surface forcing or an atmospheric ensemble is used to represent errors in surface forcing. The ensemble transform Kalman filter is used to assess the impact of adaptive observations on future analysis and forecast uncertainty for both ocean and acoustic properties.

Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.

In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based onmore » a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.« less

Wind Power Forecasting Error Distributions: An International Comparison; Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Lew, D.; Milligan, M.

2012-09-01

Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations.

Multi-Temporal Decomposed Wind and Load Power Models for Electric Energy Systems

NASA Astrophysics Data System (ADS)

Abdel-Karim, Noha

This thesis is motivated by the recognition that sources of uncertainties in electric power systems are multifold and may have potentially far-reaching effects. In the past, only system load forecast was considered to be the main challenge. More recently, however, the uncertain price of electricity and hard-to-predict power produced by renewable resources, such as wind and solar, are making the operating and planning environment much more challenging. The near-real-time power imbalances are compensated by means of frequency regulation and generally require fast-responding costly resources. Because of this, a more accurate forecast and look-ahead scheduling would result in a reduced need for expensive power balancing. Similarly, long-term planning and seasonal maintenance need to take into account long-term demand forecast as well as how the short-term generation scheduling is done. The better the demand forecast, the more efficient planning will be as well. Moreover, computer algorithms for scheduling and planning are essential in helping the system operators decide what to schedule and planners what to build. This is needed given the overall complexity created by different abilities to adjust the power output of generation technologies, demand uncertainties and by the network delivery constraints. Given the growing presence of major uncertainties, it is likely that the main control applications will use more probabilistic approaches. Today's predominantly deterministic methods will be replaced by methods which account for key uncertainties as decisions are made. It is well-understood that although demand and wind power cannot be predicted at very high accuracy, taking into consideration predictions and scheduling in a look-ahead way over several time horizons generally results in more efficient and reliable utilization, than when decisions are made assuming deterministic, often worst-case scenarios. This change is in approach is going to ultimately require new electricity market rules capable of providing the right incentives to manage uncertainties and of differentiating various technologies according to the rate at which they can respond to ever changing conditions. Given the overall need for modeling uncertainties in electric energy systems, we consider in this thesis the problem of multi-temporal modeling of wind and demand power, in particular. Historic data is used to derive prediction models for several future time horizons. Short-term prediction models derived can be used for look-ahead economic dispatch and unit commitment, while the long-term annual predictive models can be used for investment planning. As expected, the accuracy of such predictive models depends on the time horizons over which the predictions are made, as well as on the nature of uncertain signals. It is shown that predictive models obtained using the same general modeling approaches result in different accuracy for wind than for demand power. In what follows, we introduce several models which have qualitatively different patterns, ranging from hourly to annual. We first transform historic time-stamped data into the Fourier Transform (Fr) representation. The frequency domain data representation is used to decompose the wind and load power signals and to derive predictive models relevant for short-term and long-term predictions using extracted spectral techniques. The short-term results are interpreted next as a Linear Prediction Coding Model (LPC) and its accuracy is analyzed. Next, a new Markov-Based Sensitivity Model (MBSM) for short term prediction has been proposed and the dispatched costs of uncertainties for different predictive models with comparisons have been developed. Moreover, the Discrete Markov Process (DMP) representation is applied to help assess probabilities of most likely short-, medium- and long-term states and the related multi-temporal risks. In addition, this thesis discusses operational impacts of wind power integration in different scenario levels by performing more than 9,000 AC Optimal Power Flow runs. The effects of both wind and load variations on system constraints and costs are presented. The limitations of DC Optimal Power Flow (DCOPF) vs. ACOPF are emphasized by means of system convergence problems due to the effect of wind power on changing line flows and net power injections. By studying the effect of having wind power on line flows, we found that the divergence problem applies in areas with high wind and hydro generation capacity share (cheap generations). (Abstract shortened by UMI.).

Application of SELECT and SWAT models to simulate source load, fate, and transport of fecal bacteria in watersheds.

NASA Astrophysics Data System (ADS)

Ranatunga, T.

2017-12-01

Modeling of fate and transport of fecal bacteria in a watershed is a processed based approach that considers releases from manure, point sources, and septic systems. Overland transport with water and sediments, infiltration into soils, transport in the vadose zone and groundwater, die-off and growth processes, and in-stream transport are considered as the other major processes in bacteria simulation. This presentation will discuss a simulation of fecal indicator bacteria source loading and in-stream conditions of a non-tidal watershed (Cedar Bayou Watershed) in South Central Texas using two models; Spatially Explicit Load Enrichment Calculation Tool (SELECT) and Soil and Water Assessment Tool (SWAT). Furthermore, it will discuss a probable approach of bacteria source load reduction in order to meet the water quality standards in the streams. The selected watershed is listed as having levels of fecal indicator bacteria that posed a risk for contact recreation and wading by the Texas Commission of Environmental Quality (TCEQ). The SELECT modeling approach was used in estimating the bacteria source loading from land categories. Major bacteria sources considered were, failing septic systems, discharges from wastewater treatment facilities, excreta from livestock (Cattle, Horses, Sheep and Goat), excreta from Wildlife (Feral Hogs, and Deer), Pet waste (mainly from Dogs), and runoff from urban surfaces. The estimated source loads from SELECT model were input to the SWAT model, and simulate the bacteria transport through the land and in-stream. The calibrated SWAT model was then used to estimate the indicator bacteria in-stream concentrations for future years based on regional land use, population and household forecast (up to 2040). Based on the reductions required to meet the water quality standards in-stream, the corresponding required source load reductions were estimated.

Real-time emergency forecasting technique for situation management systems

NASA Astrophysics Data System (ADS)

Kopytov, V. V.; Kharechkin, P. V.; Naumenko, V. V.; Tretyak, R. S.; Tebueva, F. B.

2018-05-01

The article describes the real-time emergency forecasting technique that allows increasing accuracy and reliability of forecasting results of any emergency computational model applied for decision making in situation management systems. Computational models are improved by the Improved Brown’s method applying fractal dimension to forecast short time series data being received from sensors and control systems. Reliability of emergency forecasting results is ensured by the invalid sensed data filtering according to the methods of correlation analysis.

Louisiana Airport System Plan aviation activity forecasts 1990-2010.

DOT National Transportation Integrated Search

1991-07-01

This report documents the methodology used to develop the aviation activity forecasts prepared as a part of the update to the Louisiana Airport System Plan and provides Louisiana aviation forecasts for the years 1990 to 2010. In general, the forecast...

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, M. H.; Giebel, G.; Nielsen, T. S.; Hahmann, A.; Sørensen, P.; Madsen, H.

2012-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the working title "Integrated Wind Power Planning Tool". The project commenced October 1, 2011, and the goal is to integrate a numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. With regard to the latter, one such simulation tool has been developed at the Wind Energy Division, Risø DTU, intended for long term power system planning. As part of the PSO project the inferior NWP model used at present will be replaced by the state-of-the-art Weather Research & Forecasting (WRF) model. Furthermore, the integrated simulation tool will be improved so it can handle simultaneously 10-50 times more turbines than the present ~ 300, as well as additional atmospheric parameters will be included in the model. The WRF data will also be input for a statistical short term prediction model to be developed in collaboration with ENFOR A/S; a danish company that specialises in forecasting and optimisation for the energy sector. This integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated prediction tool constitute scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator, and the need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2020, from the current 20%.

Real-time forecasting at weekly timescales of the SST and SLA of the Ligurian Sea with a satellite-based ocean forecasting (SOFT) system

NASA Astrophysics Data System (ADS)

ÁLvarez, A.; Orfila, A.; Tintoré, J.

2004-03-01

Satellites are the only systems able to provide continuous information on the spatiotemporal variability of vast areas of the ocean. Relatively long-term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite-based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. SOFT systems can predict satellite-observed fields at different timescales. The forecast skill of SOFT systems forecasting the sea surface temperature (SST) at monthly timescales has been extensively explored in previous works. In this work we study the performance of two SOFT systems forecasting, respectively, the SST and sea level anomaly (SLA) at weekly timescales, that is, providing forecasts of the weekly averaged SST and SLA fields with 1 week in advance. The SOFT systems were implemented in the Ligurian Sea (Western Mediterranean Sea). Predictions from the SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the SOFT system forecasting the SST field is always superior in terms of predictability to persistence. Minimum prediction errors in the SST are obtained during winter and spring seasons. On the other hand, the biggest differences between the performance of SOFT and persistence models are found during summer and autumn. These changes in the predictability are explained on the basis of the particular variability of the SST field in the Ligurian Sea. Concerning the SLA field, no improvements with respect to persistence have been found for the SOFT system forecasting the SLA field.

Building Energy Modeling and Control Methods for Optimization and Renewables Integration

NASA Astrophysics Data System (ADS)

Burger, Eric M.

This dissertation presents techniques for the numerical modeling and control of building systems, with an emphasis on thermostatically controlled loads. The primary objective of this work is to address technical challenges related to the management of energy use in commercial and residential buildings. This work is motivated by the need to enhance the performance of building systems and by the potential for aggregated loads to perform load following and regulation ancillary services, thereby enabling the further adoption of intermittent renewable energy generation technologies. To increase the generalizability of the techniques, an emphasis is placed on recursive and adaptive methods which minimize the need for customization to specific buildings and applications. The techniques presented in this dissertation can be divided into two general categories: modeling and control. Modeling techniques encompass the processing of data streams from sensors and the training of numerical models. These models enable us to predict the energy use of a building and of sub-systems, such as a heating, ventilation, and air conditioning (HVAC) unit. Specifically, we first present an ensemble learning method for the short-term forecasting of total electricity demand in buildings. As the deployment of intermittent renewable energy resources continues to rise, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. Second, we present a recursive parameter estimation technique for identifying a thermostatically controlled load (TCL) model that is non-linear in the parameters. For TCLs to perform demand response services in real-time markets, online methods for parameter estimation are needed. Third, we develop a piecewise linear thermal model of a residential building and train the model using data collected from a custom-built thermostat. This model is capable of approximating unmodeled dynamics within a building by learning from sensor data. Control techniques encompass the application of optimal control theory, model predictive control, and convex distributed optimization to TCLs. First, we present the alternative control trajectory (ACT) representation, a novel method for the approximate optimization of non-convex discrete systems. This approach enables the optimal control of a population of non-convex agents using distributed convex optimization techniques. Second, we present a distributed convex optimization algorithm for the control of a TCL population. Experimental results demonstrate the application of this algorithm to the problem of renewable energy generation following. This dissertation contributes to the development of intelligent energy management systems for buildings by presenting a suite of novel and adaptable modeling and control techniques. Applications focus on optimizing the performance of building operations and on facilitating the integration of renewable energy resources.

Great Lakes Maps - NOAA's National Weather Service

Science.gov Websites

Coastal Forecast System) Waves (GLERL Great Lakes Coastal Forecast System) Ice Cover (GLERL Great Lakes Coastal Forecast System) NOAA's National Weather Service Central Region Headquarters Regional Office 7220

DOE Office of Scientific and Technical Information (OSTI.GOV)

Youssef, Tarek A.; El Hariri, Mohamad; Elsayed, Ahmed T.

The smart grid is seen as a power system with realtime communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service (DDS) Ethernet network withmore » appropriate quality of service (QoS) profiles. In conclusion, the developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles.« less

Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

NASA Astrophysics Data System (ADS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie

2014-03-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps.

3D cloud detection and tracking system for solar forecast using multiple sky imagers

DOE PAGES

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

2015-06-23

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

Assessing the viability of `over-the-loop' real-time short-to-medium range ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, E.; Mendoza, P. A.; Nijssen, B.; Newman, A. J.; Clark, M. P.; Arnold, J.; Nowak, K. C.

2016-12-01

Many if not most national operational short-to-medium range streamflow prediction systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow are automated, but others require the hands-on-effort of an experienced human forecaster. This approach evolved out of the need to correct for deficiencies in the models and datasets that were available for forecasting, and often leads to skillful predictions despite the use of relatively simple, conceptual models. On the other hand, the process is not reproducible, which limits opportunities to assess and incorporate process variations, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast ensembles and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun to develop more centralized, `over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, the operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as the systems are being rolled out in major operational forecasting centers. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis, Research, and Prediction' (SHARP) to implement, assess and demonstrate real-time over-the-loop forecasts. We present early hindcast and verification results from SHARP for short to medium range streamflow forecasts in a number of US case study watersheds.

First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

2017-04-01

Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for improvements are currently being addressed in the system next update.

Design of a Forecasting Service System for Monitoring of Vulnerabilities of Sensor Networks

NASA Astrophysics Data System (ADS)

Song, Jae-Gu; Kim, Jong Hyun; Seo, Dong Il; Kim, Seoksoo

This study aims to reduce security vulnerabilities of sensor networks which transmit data in an open environment by developing a forecasting service system. The system is to remove or monitor causes of breach incidents in advance. To that end, this research first examines general security vulnerabilities of sensor networks and analyzes characteristics of existing forecasting systems. Then, 5 steps of a forecasting service system are proposed in order to improve security responses.

Online Optimization Method for Operation of Generators in a Micro Grid

NASA Astrophysics Data System (ADS)

Hayashi, Yasuhiro; Miyamoto, Hideki; Matsuki, Junya; Iizuka, Toshio; Azuma, Hitoshi

Recently a lot of studies and developments about distributed generator such as photovoltaic generation system, wind turbine generation system and fuel cell have been performed under the background of the global environment issues and deregulation of the electricity market, and the technique of these distributed generators have progressed. Especially, micro grid which consists of several distributed generators, loads and storage battery is expected as one of the new operation system of distributed generator. However, since precipitous load fluctuation occurs in micro grid for the reason of its smaller capacity compared with conventional power system, high-accuracy load forecasting and control scheme to balance of supply and demand are needed. Namely, it is necessary to improve the precision of operation in micro grid by observing load fluctuation and correcting start-stop schedule and output of generators online. But it is not easy to determine the operation schedule of each generator in short time, because the problem to determine start-up, shut-down and output of each generator in micro grid is a mixed integer programming problem. In this paper, the authors propose an online optimization method for the optimal operation schedule of generators in micro grid. The proposed method is based on enumeration method and particle swarm optimization (PSO). In the proposed method, after picking up all unit commitment patterns of each generators satisfied with minimum up time and minimum down time constraint by using enumeration method, optimal schedule and output of generators are determined under the other operational constraints by using PSO. Numerical simulation is carried out for a micro grid model with five generators and photovoltaic generation system in order to examine the validity of the proposed method.

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

NASA Astrophysics Data System (ADS)

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.; Galelli, Stefano

2017-09-01

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made - namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strongmore » relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.« less

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

DOE PAGES

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.; ...

2017-09-28

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strongmore » relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.« less

Progress and challenges with Warn-on-Forecast

NASA Astrophysics Data System (ADS)

Stensrud, David J.; Wicker, Louis J.; Xue, Ming; Dawson, Daniel T.; Yussouf, Nusrat; Wheatley, Dustan M.; Thompson, Therese E.; Snook, Nathan A.; Smith, Travis M.; Schenkman, Alexander D.; Potvin, Corey K.; Mansell, Edward R.; Lei, Ting; Kuhlman, Kristin M.; Jung, Youngsun; Jones, Thomas A.; Gao, Jidong; Coniglio, Michael C.; Brooks, Harold E.; Brewster, Keith A.

2013-04-01

The current status and challenges associated with two aspects of Warn-on-Forecast-a National Oceanic and Atmospheric Administration research project exploring the use of a convective-scale ensemble analysis and forecast system to support hazardous weather warning operations-are outlined. These two project aspects are the production of a rapidly-updating assimilation system to incorporate data from multiple radars into a single analysis, and the ability of short-range ensemble forecasts of hazardous convective weather events to provide guidance that could be used to extend warning lead times for tornadoes, hailstorms, damaging windstorms and flash floods. Results indicate that a three-dimensional variational assimilation system, that blends observations from multiple radars into a single analysis, shows utility when evaluated by forecasters in the Hazardous Weather Testbed and may help increase confidence in a warning decision. The ability of short-range convective-scale ensemble forecasts to provide guidance that could be used in warning operations is explored for five events: two tornadic supercell thunderstorms, a macroburst, a damaging windstorm and a flash flood. Results show that the ensemble forecasts of the three individual severe thunderstorm events are very good, while the forecasts from the damaging windstorm and flash flood events, associated with mesoscale convective systems, are mixed. Important interactions between mesoscale and convective-scale features occur for the mesoscale convective system events that strongly influence the quality of the convective-scale forecasts. The development of a successful Warn-on-Forecast system will take many years and require the collaborative efforts of researchers and operational forecasters to succeed.

Challenges for operational forecasting and early warning of rainfall induced landslides

NASA Astrophysics Data System (ADS)

Guzzetti, Fausto

2017-04-01

In many areas of the world, landslides occur every year, claiming lives and producing severe economic and environmental damage. Many of the landslides with human or economic consequences are the result of intense or prolonged rainfall. For this reason, in many areas the timely forecast of rainfall-induced landslides is of both scientific interest and social relevance. In the recent years, there has been a mounting interest and an increasing demand for operational landslide forecasting, and for associated landslide early warning systems. Despite the relevance of the problem, and the increasing interest and demand, only a few systems have been designed, and are currently operated. Inspection of the - limited - literature on operational landslide forecasting, and on the associated early warning systems, reveals that common criteria and standards for the design, the implementation, the operation, and the evaluation of the performances of the systems, are lacking. This limits the possibility to compare and to evaluate the systems critically, to identify their inherent strengths and weaknesses, and to improve the performance of the systems. Lack of common criteria and of established standards can also limit the credibility of the systems, and consequently their usefulness and potential practical impact. Landslides are very diversified phenomena, and the information and the modelling tools used to attempt landslide forecasting vary largely, depending on the type and size of the landslides, the extent of the geographical area considered, the timeframe of the forecasts, and the scope of the predictions. Consequently, systems for landslide forecasting and early warning can be designed and implemented at several different geographical scales, from the local (site or slope specific) to the regional, or even national scale. The talk focuses on regional to national scale landslide forecasting systems, and specifically on operational systems based on empirical rainfall threshold models. Building on the experience gained in designing, implementing, and operating national and regional landslide forecasting systems in Italy, and on a preliminary review of the existing literature on regional landslide early warning systems, the talk discusses concepts, limitations and challenges inherent to the design of reliable forecasting and early warning systems for rainfall-triggered landslides, the evaluation of the performances of the systems, and on problems related to the use of the forecasts and the issuing of landslide warnings. Several of the typical elements of an operational landslide forecasting system are considered, including: (i) the rainfall and landslide information used to establish the threshold models, (ii) the methods and tools used to define the empirical rainfall thresholds, and their associated uncertainty, (iii) the quality (e.g., the temporal and spatial resolution) of the rainfall information used for operational forecasting, including rain gauge and radar measurements, satellite estimates, and quantitative weather forecasts, (iv) the ancillary information used to prepare the forecasts, including e.g., the terrain subdivisions and the landslide susceptibility zonations, (v) the criteria used to transform the forecasts into landslide warnings and the methods used to communicate the warnings, and (vi) the criteria and strategies adopted to evaluate the performances of the systems, and to define minimum or optimal performance levels.

Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

NASA Astrophysics Data System (ADS)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the MizuRoute channel routing tool) but also distributed model states such as soil moisture and snow water equivalent. We also describe challenges in distributed model-based forecasting, including the application and early results of real-time hydrologic data assimilation.

Validation and Inter-comparison Against Observations of GODAE Ocean View Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xu, J.; Davidson, F. J. M.; Smith, G. C.; Lu, Y.; Hernandez, F.; Regnier, C.; Drevillon, M.; Ryan, A.; Martin, M.; Spindler, T. D.; Brassington, G. B.; Oke, P. R.

2016-02-01

For weather forecasts, validation of forecast performance is done at the end user level as well as by the meteorological forecast centers. In the development of Ocean Prediction Capacity, the same level of care for ocean forecast performance and validation is needed. Herein we present results from a validation against observations of 6 Global Ocean Forecast Systems under the GODAE OceanView International Collaboration Network. These systems include the Global Ocean Ice Forecast System (GIOPS) developed by the Government of Canada, two systems PSY3 and PSY4 from the French Mercator-Ocean Ocean Forecasting Group, the FOAM system from UK met office, HYCOM-RTOFS from NOAA/NCEP/NWA of USA, and the Australian Bluelink-OceanMAPS system from the CSIRO, the Australian Meteorological Bureau and the Australian Navy.The observation data used in the comparison are sea surface temperature, sub-surface temperature, sub-surface salinity, sea level anomaly, and sea ice total concentration data. Results of the inter-comparison demonstrate forecast performance limits, strengths and weaknesses of each of the six systems. This work establishes validation protocols and routines by which all new prediction systems developed under the CONCEPTS Collaborative Network will be benchmarked prior to approval for operations. This includes anticipated delivery of CONCEPTS regional prediction systems over the next two years including a pan Canadian 1/12th degree resolution ice ocean prediction system and limited area 1/36th degree resolution prediction systems. The validation approach of comparing forecasts to observations at the time and location of the observation is called Class 4 metrics. It has been adopted by major international ocean prediction centers, and will be recommended to JCOMM-WMO as routine validation approach for operational oceanography worldwide.

4 km Forecasting System to Support DISCOVER-AQ Campaigns: Model Configuration, Testing and Evaluation

NASA Astrophysics Data System (ADS)

Lee, P.; Pan, L.; Kim, H. C.; Chai, T.; Hu, Y.; Tong, D.; Ngan, F.; Wong, D.; Dornblaser, B.; Tanrikulu, S.; Pickering, K. E.

2012-12-01

This work presents the development and evaluation of a high-resolution air quality forecasting system to support two NASA Earth Venture campaigns (DISCOVER-AQ) in 2013. These campaigns aim to further understanding of column-integrated and vertically resolved observations in determining air pollution conditions near the surface (http://science.nasa.gov/missions/discover-aq/). The first one will be carried out in San Joaquin Valley (SJV) in winter and the second one in Houston (HOU) area in late summer. Accurate forecast of pollution plumes is critical for on-site deployment and co-ordination of the various observation platforms. We develop of a fine resolution forecasting system to provide dynamic prediction of the chemical fields over these regions. This system utilizes meteorology fields from the US National Centers for Environmental Prediction North American Model (NAM) that is equipped with an elaborative NAM Data Assimilation System (NDAS) for its Land Surface Model (LSM) and initialization processes. NAM output is used to drive the US EPA Community Multi-scale Air Quality Model (CMAQ) with identical horizontal resolution. The SJV campaign is believed to be subjected to rather high particulate matter loading and possible frequent occurrence of multiple-day fog. NDAS provides advanced methodology to constrain atmospheric stability and soil moisture characteristics. These meteorological parameters are critical for the winter campaign. Special attention is paid to emission modeling for agricultural dust aerosols, which were found important for the SJV area. In contrast to the winter campaign where strong atmospheric stability will likely be a challenge, the HOU campaign in September of 2013 will be challenged with strong atmospheric convection and rather rapid growth of and a sustained deep Planetary Boundary Layer (PBL) during mid-morning and afternoon, respectively. Convection often results in lightning. Wild-fires can contribute significantly to pollution. Therefore both climatology lightning NOx and real-time fires observed by the Hazardous Mapping System by National Environmental Satellite, Data and Information Service (NESDIS) will be included as emission sources. We have performed a real-time test for the HOU area. We will show model evaluation and post-campaign sensitivity modeling results to shed additional insight on processes responsible for the characteristics of the pollutant concentrations.

Consensus Seasonal Flood Forecasts and Warning Response System (FFWRS): an alternate for nonstructural flood management in Bangladesh.

PubMed

Chowdhury, Rashed

2005-06-01

Despite advances in short-range flood forecasting and information dissemination systems in Bangladesh, the present system is less than satisfactory. This is because of short lead-time products, outdated dissemination networks, and lack of direct feedback from the end-user. One viable solution is to produce long-lead seasonal forecasts--the demand for which is significantly increasing in Bangladesh--and disseminate these products through the appropriate channels. As observed in other regions, the success of seasonal forecasts, in contrast to short-term forecast, depends on consensus among the participating institutions. The Flood Forecasting and Warning Response System (henceforth, FFWRS) has been found to be an important component in a comprehensive and participatory approach to seasonal flood management. A general consensus in producing seasonal forecasts can thus be achieved by enhancing the existing FFWRS. Therefore, the primary objective of this paper is to revisit and modify the framework of an ideal warning response system for issuance of consensus seasonal flood forecasts in Bangladesh. The five-stage FFWRS-i) Flood forecasting, ii) Forecast interpretation and message formulation, iii) Warning preparation and dissemination, iv) Responses, and v) Review and analysis-has been modified. To apply the concept of consensus forecast, a framework similar to that of the Southern African Regional Climate Outlook Forum (SARCOF) has been discussed. Finally, the need for a climate Outlook Fora has been emphasized for a comprehensive and participatory approach to seasonal flood hazard management in Bangladesh.

A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

NASA Astrophysics Data System (ADS)

Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

2017-12-01

The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.

A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

NASA Astrophysics Data System (ADS)

Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Many if not most national operational streamflow prediction systems rely on a forecaster-in-the-loop approach that require the hands-on-effort of an experienced human forecaster. This approach evolved from the need to correct for long-standing deficiencies in the models and datasets used in forecasting, and the practice often leads to skillful flow predictions despite the use of relatively simple, conceptual models. Yet the 'in-the-loop' forecast process is not reproducible, which limits opportunities to assess and incorporate new techniques systematically, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun develop more centralized, 'over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, many national operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as such systems are beginning to be deployed operationally in centers such as ECMWF. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the US National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis Research and Prediction Applications' (SHARP) to implement, assess and demonstrate real-time over-the-loop ensemble flow forecasts in a range of US watersheds. The system relies on fully ensemble techniques, including: an 100-member ensemble of meteorological model forcings and an ensemble particle filter data assimilation for initializing watershed states; analog/regression-based downscaling of ensemble weather forecasts from GEFS; and statistical post-processing of ensemble forecast outputs, all of which run in real-time within a workflow managed by ECWMF's ecFlow libraries over large US regional domains. We describe SHARP and present early hindcast and verification results for short to seasonal range streamflow forecasts in a number of US case study watersheds.

Forecasting, Forecasting

Treesearch

Michael A. Fosberg

1987-01-01

Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

Skill of a global seasonal ensemble streamflow forecasting system

NASA Astrophysics Data System (ADS)

Candogan Yossef, Naze; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc

2013-04-01

Forecasting of water availability and scarcity is a prerequisite for managing the risks and opportunities caused by the inter-annual variability of streamflow. Reliable seasonal streamflow forecasts are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological forecasting on a global scale could be valuable especially for developing regions of the world, where effective hydrological forecasting systems are scarce. In this study, we investigate the forecasting skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCR-GLOBWB. FEWS-World has been setup within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The assessment in historical simulation mode used a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF). We assessed the skill of the global hydrological model PCR-GLOBWB in reproducing past discharge extremes in 20 large rivers of the world. This preliminary assessment concluded that the prospects for seasonal forecasting with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological forecasts. Thus the meteorological forcing errors were not assessed. Yet, in a forecasting setup, the predictive skill of a hydrological forecasting system is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive forecasting mode, the model is forced with actual ensemble forecasts from the seasonal forecast archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of the year and for lead times up to 6 months. The forecasted discharges are compared with observed monthly streamflow records using the ensemble verification measures Brier Skill Score (BSS) and Continuous Ranked Probability Score (CRPS). The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world.

Risky Business: Development, Communication and Use of Hydroclimatic Forecasts

NASA Astrophysics Data System (ADS)

Lall, U.

2012-12-01

Inter-seasonal and longer hydroclimatic forecasts have been made increasingly in the last two decades following the increase in ENSO activity since the early 1980s and the success in seasonal ENSO forecasting. Yet, the number of examples of systematic use of these forecasts and their incorporation into water systems operation continue to be few. This may be due in part to the limited skill in such forecasts over much of the world, but is also likely due to the limited evolution of methods and opportunities to "safely" use uncertain forecasts. There has been a trend to rely more on "physically based" rather than "physically informed" empirical forecasts, and this may in part explain the limited success in developing usable products in more locations. Given the limited skill, forecasters have tended to "dumb" down their forecasts - either formally or subjectively shrinking the forecasts towards climatology, or reducing them to tercile forecasts that serve to obscure the potential information in the forecast. Consequently, the potential utility of such forecasts for decision making is compromised. Water system operating rules are often designed to be robust in the face of historical climate variability, and consequently are adapted to the potential conditions that a forecast seeks to inform. In such situations, there is understandable reluctance by managers to use the forecasts as presented, except in special cases where an alternate course of action is pragmatically appealing in any case. In this talk, I review opportunities to present targeted forecasts for use with decision systems that directly address climate risk and the risk induced by unbiased yet uncertain forecasts, focusing especially on extreme events and water allocation in a competitive environment. Examples from Brazil and India covering surface and ground water conjunctive use strategies that could potentially be insured and lead to improvements over the traditional system operation and resource allocation are provided.

A global flash flood forecasting system

NASA Astrophysics Data System (ADS)

Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

2016-04-01

The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial resolution appropriate to the NWP system. We then demonstrate how these warning areas could eventually complement existing global systems such as the Global Flood Awareness System (GloFAS), to give warnings of flash floods. This work demonstrates the possibility of creating a global flash flood forecasting system based on forecasts from existing global NWP systems. Future developments, in post-processing for example, will need to address an under-prediction bias, for extreme point rainfall, that is innate to current-generation global models.

A probabilistic drought forecasting framework: A combined dynamical and statistical approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initialmore » condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.« less

Development, Implementation, and Skill Assessment of the NOAA/NOS Great Lakes Operational Forecast System

DTIC Science & Technology

2011-01-01

USA) 2011 Abstract The NOAA Great Lakes Operational Forecast System ( GLOFS ) uses near-real-time atmospheric observa- tions and numerical weather...Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS... GLOFS ) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2013-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography. These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi-model ensemble forecasting, to U.S. Department of Defense use, and creating a superior U.S. global meteorological and oceanographic prediction capability. Forecast verification is an important component of NAEFS and NUOPC. Distribution Statement A: Approved for Public Release; distribution is unlimited

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S. P.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2012-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography (METOC). These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi-model ensemble forecasting, to U.S. Department of Defense use, and creating a superior U.S. global meteorological and oceanographic prediction capability. Forecast verification is an important component of NAEFS and NUOPC.

Assessment of the Charging Policy in Energy Efficiency of the Enterprise

NASA Astrophysics Data System (ADS)

Shutov, E. A.; E Turukina, T.; Anisimov, T. S.

2017-04-01

The forecasting problem for energy facilities with a power exceeding 670 kW is currently one of the main. In connection with rules of the retail electricity market such customers also pay for actual energy consumption deviations from plan value. In compliance with the hierarchical stages of the electricity market a guaranteeing supplier is to respect the interests of distribution and generation companies that require load leveling. The answer to this question for industrial enterprise is possible only within technological process through implementation of energy-efficient processing chains with the adaptive function and forecasting tool. In such a circumstance the primary objective of a forecasting is reduce the energy consumption costs by taking account of the energy cost correlation for 24 hours for forming of pumping unit work schedule. The pumping unit virtual model with the variable frequency drive is considered. The forecasting tool and the optimizer are integrated into typical control circuit. Economic assessment of the optimization method was estimated.

The Impact of Implementing a Demand Forecasting System into a Low-Income Country’s Supply Chain

PubMed Central

Mueller, Leslie E.; Haidari, Leila A.; Wateska, Angela R.; Phillips, Roslyn J.; Schmitz, Michelle M.; Connor, Diana L.; Norman, Bryan A.; Brown, Shawn T.; Welling, Joel S.; Lee, Bruce Y.

2016-01-01

OBJECTIVE To evaluate the potential impact and value of applications (e.g., ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country’s vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS Using our software, HERMES, we generated a detailed discrete event simulation model of Niger’s entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION Demand forecasting systems have the potential to greatly improve vaccine demand fulfillment, and decrease logistics cost/dose when implemented with storage and transportation increases direct vaccines. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. PMID:27219341

The impact of implementing a demand forecasting system into a low-income country's supply chain.

PubMed

Mueller, Leslie E; Haidari, Leila A; Wateska, Angela R; Phillips, Roslyn J; Schmitz, Michelle M; Connor, Diana L; Norman, Bryan A; Brown, Shawn T; Welling, Joel S; Lee, Bruce Y

2016-07-12

To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. Copyright © 2016 Elsevier Ltd. All rights reserved.

Experimental Forecasts of Wildfire Pollution at the Canadian Meteorological Centre

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Beaulieu, Paul-Andre; Chen, Jack; Landry, Hugo; Cousineau, Sophie; Moran, Michael

2016-04-01

Environment and Climate Change Canada's Canadian Meteorological Centre Operations division (CMCO) has been running an experimental North American air quality forecast system with near-real-time wildfire emissions since 2014. This system, named FireWork, also takes anthropogenic and other natural emission sources into account. FireWork 48-hour forecasts are provided to CMCO forecasters and external partners in Canada and the U.S. twice daily during the wildfire season. This system has proven to be very useful in capturing short- and long-range smoke transport from wildfires over North America. Several upgrades to the FireWork system have been made since 2014 to accommodate the needs of operational AQ forecasters and to improve system performance. In this talk we will present performance statistics and some case studies for the 2014 and 2015 wildfire seasons. We will also describe current limitations of the FireWork system and ongoing and future work planned for this air quality forecast system.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Cui, Mingjian; Hodge, Bri-Mathias

The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most ofmore » the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.« less

Evaluation of weather forecast systems for storm surge modeling in the Chesapeake Bay

NASA Astrophysics Data System (ADS)

Garzon, Juan L.; Ferreira, Celso M.; Padilla-Hernandez, Roberto

2018-01-01

Accurate forecast of sea-level heights in coastal areas depends, among other factors, upon a reliable coupling of a meteorological forecast system to a hydrodynamic and wave system. This study evaluates the predictive skills of the coupled circulation and wind-wave model system (ADCIRC+SWAN) for simulating storm tides in the Chesapeake Bay, forced by six different products: (1) Global Forecast System (GFS), (2) Climate Forecast System (CFS) version 2, (3) North American Mesoscale Forecast System (NAM), (4) Rapid Refresh (RAP), (5) European Center for Medium-Range Weather Forecasts (ECMWF), and (6) the Atlantic hurricane database (HURDAT2). This evaluation is based on the hindcasting of four events: Irene (2011), Sandy (2012), Joaquin (2015), and Jonas (2016). By comparing the simulated water levels to observations at 13 monitoring stations, we have found that the ADCIR+SWAN System forced by the following: (1) the HURDAT2-based system exhibited the weakest statistical skills owing to a noteworthy overprediction of the simulated wind speed; (2) the ECMWF, RAP, and NAM products captured the moment of the peak and moderately its magnitude during all storms, with a correlation coefficient ranging between 0.98 and 0.77; (3) the CFS system exhibited the worst averaged root-mean-square difference (excepting HURDAT2); (4) the GFS system (the lowest horizontal resolution product tested) resulted in a clear underprediction of the maximum water elevation. Overall, the simulations forced by NAM and ECMWF systems induced the most accurate results best accuracy to support water level forecasting in the Chesapeake Bay during both tropical and extra-tropical storms.

Forecasting selenium discharges to the San Francisco Bay-Delta Estuary: ecological effects of a proposed San Luis drain extension

USGS Publications Warehouse

Luoma, Samuel N.; Presser, Theresa S.

2000-01-01

During the next few years, federal and state agencies may be required to evaluate proposals and discharge permits that could significantly change selenium (Se) inputs to the San Francisco Bay-Delta Estuary (Bay-Delta), particularly in the North Bay (i.e., Suisun Bay and San Pablo Bay). These decisions may include discharge requirements for an extension of the San Luis Drain (SLD) to the estuary to convey subsurface agricultural drainage from the western San Joaquin Valley (SJV), a renewal of an agreement to allow the existing portion of the SLD to convey subsurface agricultural drainage to a tributary of the San Joaquin River (SJR) (coincident with changes in flow patterns of the lower SJR), and refinements to promulgated Se criteria for the protection of aquatic life for the estuary. Understanding the biotransfer of Se is essential to evaluating the fate and impact of proposed changes in Se discharges to the Bay-Delta. However, past monitoring programs have not addressed the specific protocols necessary for an element that bioaccumulates. Confusion about Se threats in the past have stemmed from failure to consider the full complexity of the processes that result in Se toxicity. Past studies show that predators are more at risk from Se contamination than their prey, making it difficult to use traditional methods to predict risk from environmental concentrations alone. In this report, we employ a novel procedure to model the fate of Se under different, potentially realistic load scenarios from the SJV. For each potential load, we progressively forecast the resulting environmental concentrations, speciation, transformation to particulate form, bioaccumulation by invertebrates, trophic transfer to predators, and effects in those predators. Enough is known to establish a first order understanding of effects should Se be discharged directly into the North Bay via a conveyance such as the SLD. Our approach uses 1) existing knowledge concerning the biogeochemical reactions of Se (e.g., speciation, partitioning between dissolved and particulate forms, and bivalve assimilation efficiency) and 2) site-specific data mainly from 1986 to 1996 on clams and bottom-feeding fish and birds. Forecasts of Se loading from oil refineries and agricultural drainage from the SJV enable the calculation of a composite freshwater endmember Se concentration at the head of the estuary and at Carquinez Strait as a foundation for modeling. Our analysis of effects also takes into account the mode of conveyance for agricultural drainage (i.e., the SLD or SJR). The effects of variable flows on a seasonal or monthly basis from the Sacramento River and SJR are also considered. The results of our forecasts for external SJV watershed sources of Se mirror predictions made since 1955 of a worsening salt (and by inference, Se) buildup exacerbated by the arid climate and irrigation for agricultural use. We show that the reservoir of Se in the SJV is sufficient to provide loading at an annual rate of approximately 42,500 pounds (lbs) of Se to a Bay-Delta disposal point for 63 to 304 years at the lower range of our projections, even if influx of Se from the California Coast Ranges could be curtailed. Disposal of wastewaters on an annual basis outside of the SJV may slow the degradation of valley resources, but drainage alone cannot alleviate the salt and Se buildup in the SJV, at least within a century. Our forecasts show the different proportions of Se loading to the Bay-Delta. Oil refinery loads from 1986 to 1992 ranged from 11 to 15 lbs Se per day; with treatment and cleanup, loads decreased to 3 lbs Se per day in 1999. In contrast, SJV agricultural drainage loads could range from of 45 to 117 lbs Se per day across a set of reasonable conditions. Components of this valley-wide load include five source subareas (i.e., Grassland, Westlands, Tulare, Kern, and Northern) based on water and drainage management. Loads vary per subarea mainly because of proximity of the s

Seasonal Forecasting of Reservoir Inflow for the Segura River Basin, Spain

NASA Astrophysics Data System (ADS)

de Tomas, Alberto; Hunink, Johannes

2017-04-01

A major threat to the agricultural sector in Europe is an increasing occurrence of low water availability for irrigation, affecting the local and regional food security and economies. Especially in the Mediterranean region, such as in the Segura river basin (Spain), drought epidodes are relatively frequent. Part of the irrigation water demand in this basin is met by a water transfer from the Tagus basin (central Spain), but also in this basin an increasing pressure on the water resources has reduced the water available to be transferred. Currently, Drought Management Plans in these Spanish basins are in place and mitigate the impact of drought periods to some extent. Drought indicators that are derived from the available water in the storage reservoirs impose a set of drought mitigation measures. Decisions on water transfers are dependent on a regression-based time series forecast from the reservoir inflows of the preceding months. This user-forecast has its limitations and can potentially be improved using more advanced techniques. Nowadays, seasonal climate forecasts have shown to have increasing skill for certain areas and for certain applications. So far, such forecasts have not been evaluated in a seasonal hydrologic forecasting system in the Spanish context. The objective of this work is to develop a prototype of a Seasonal Hydrologic Forecasting System and compare this with a reference forecast. The reference forecast in this case is the locally used regression-based forecast. Additionally, hydrological simulations derived from climatological reanalysis (ERA-Interim) are taken as a reference forecast. The Spatial Processes in Hydrology model (SPHY - http://www.sphy.nl/) forced with the ECMWF- SFS4 (15 ensembles) Seasonal Forecast Systems is used to predict reservoir inflows of the upper basins of the Segura and Tagus rivers. The system is evaluated for 4 seasons with a forecasting lead time of 3 months. First results show that only for certain initialization months and lead times, the developed system outperforms the reference forecast. This research is carried out within the European research project IMPREX (www.imprex.eu) that aims at investigating the value of improving predictions of hydro-meteorological extremes in a number of water sectors, including agriculture . The next step is to integrate improved seasonal forecasts into the system and evaluate these. This should finally lead to a more robust forecasting system that allows water managers and irrigators to better anticipate to drought episodes and putting into practice more effective water allocation and mitigation practices.

The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps

NASA Astrophysics Data System (ADS)

Liechti, K.; Panziera, L.; Germann, U.; Zappa, M.

2013-10-01

This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA) initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.

Centralized Storm Information System (CSIS)

NASA Technical Reports Server (NTRS)

Norton, C. C.

1985-01-01

A final progress report is presented on the Centralized Storm Information System (CSIS). The primary purpose of the CSIS is to demonstrate and evaluate real time interactive computerized data collection, interpretation and display techniques as applied to severe weather forecasting. CSIS objectives pertaining to improved severe storm forecasting and warning systems are outlined. The positive impact that CSIS has had on the National Severe Storms Forecast Center (NSSFC) is discussed. The benefits of interactive processing systems on the forecasting ability of the NSSFC are described.

Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

NASA Astrophysics Data System (ADS)

Peters-Lidard, C. D.; Arsenault, K. R.; Shukla, S.; Getirana, A.; McNally, A.; Koster, R. D.; Zaitchik, B. F.; Badr, H. S.; Roningen, J. M.; Kumar, S.; Funk, C. C.

2017-12-01

A seamless and effective water deficit monitoring and early warning system is critical for assessing food security in Africa and the Middle East. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of drought and water availability monitoring products in the region. Next, it will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the products through NASA's web-services. The water deficit forecasting system thus far incorporates NASA GMAO's Catchment and the Noah Multi-Physics (MP) LSMs. In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. To establish a climatology from 1981-2015, the two LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. Comparison of the models' energy and hydrological budgets with independent observations suggests that major droughts are well-reflected in the climatology. The system uses seasonal climate forecasts from NASA's GEOS-5 (the Goddard Earth Observing System Model-5) and NCEP's Climate Forecast System-2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region. Current work suggests that for the Blue Nile basin, (1) the combination of GEOS-5 and CFSv2 is equivalent in skill to the full North American Multimodel Ensemble (NMME); and (2) the seasonal water deficit forecasting system skill for both soil moisture and streamflow anomalies is greater than the standard Ensemble Streamflow Prediction (ESP) approach.

Foresee: A user-centric home energy management system for energy efficiency and demand response

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xin; Baker, Kyri A.; Christensen, Dane T.

This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliancemore » models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.« less

Foresee: A user-centric home energy management system for energy efficiency and demand response

DOE PAGES

Jin, Xin; Baker, Kyri A.; Christensen, Dane T.; ...

2017-08-23

This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliancemore » models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.« less

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2010 CFR

2010-01-01

... borrower developed an adequate supporting database and analyzed a reasonable range of relevant assumptions and alternative futures; (d) The borrower adopted methods and procedures in general use by the...

Evaluation of TxDOT'S traffic data collection and load forecasting process

DOT National Transportation Integrated Search

2001-01-01

This study had two primary objectives: (1) compare current Texas Department of Transportation (TxDOT) procedures and protocols with the state-of-the-practice and the needs of its data customers; and (2) develop enhanced traffic collection, archival, ...

Potential for malaria seasonal forecasting in Africa

NASA Astrophysics Data System (ADS)

Tompkins, Adrian; Di Giuseppe, Francesca; Colon-Gonzalez, Felipe; Namanya, Didas; Friday, Agabe

2014-05-01

As monthly and seasonal dynamical prediction systems have improved their skill in the tropics over recent years, there is now the potential to use these forecasts to drive dynamical malaria modelling systems to provide early warnings in epidemic and meso-endemic regions. We outline a new pilot operational system that has been developed at ECMWF and ICTP. It uses a precipitation bias correction methodology to seamlessly join the monthly ensemble prediction system (EPS) and seasonal (system 4) forecast systems of ECMWF together. The resulting temperature and rainfall forecasts for Africa are then used to drive the recently developed ICTP malaria model known as VECTRI. The resulting coupled system of ECMWF climate forecasts and VECTRI thus produces predictions of malaria prevalence rates and transmission intensity across Africa. The forecasts are filtered to highlight the regions and months in which the system has particular value due to high year to year variability. In addition to epidemic areas, these also include meso and hyper-endemic regions which undergo considerable variability in the onset months. We demonstrate the limits of the forecast skill as a function of lead-time, showing that for many areas the dynamical system can add one to two months additional warning time to a system based on environmental monitoring. We then evaluate the past forecasts against district level case data in Uganda and show that when interventions can be discounted, the system can show significant skill at predicting interannual variability in transmission intensity up to 3 or 4 months ahead at the district scale. The prospects for a operational implementation will be briefly discussed.

The value of information as applied to the Landsat Follow-on benefit-cost analysis

NASA Technical Reports Server (NTRS)

Wood, D. B.

1978-01-01

An econometric model was run to compare the current forecasting system with a hypothetical (Landsat Follow-on) space-based system. The baseline current system was a hybrid of USDA SRS domestic forecasts and the best known foreign data. The space-based system improved upon the present Landsat by the higher spatial resolution capability of the thematic mapper. This satellite system is a major improvement for foreign forecasts but no better than SRS for domestic forecasts. The benefit analysis was concentrated on the use of Landsat Follow-on to forecast world wheat production. Results showed that it was possible to quantify the value of satellite information and that there are significant benefits in more timely and accurate crop condition information.

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.

A DDS-Based Energy Management Framework for Small Microgrid Operation and Control

DOE PAGES

Youssef, Tarek A.; El Hariri, Mohamad; Elsayed, Ahmed T.; ...

2017-09-26

The smart grid is seen as a power system with realtime communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service (DDS) Ethernet network withmore » appropriate quality of service (QoS) profiles. In conclusion, the developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles.« less

Development and validation of a regional coupled forecasting system for S2S forecasts

NASA Astrophysics Data System (ADS)

Sun, R.; Subramanian, A. C.; Hoteit, I.; Miller, A. J.; Ralph, M.; Cornuelle, B. D.

2017-12-01

Accurate and efficient forecasting of oceanic and atmospheric circulation is essential for a wide variety of high-impact societal needs, including: weather extremes; environmental protection and coastal management; management of fisheries, marine conservation; water resources; and renewable energy. Effective forecasting relies on high model fidelity and accurate initialization of the models with observed state of the ocean-atmosphere-land coupled system. A regional coupled ocean-atmosphere model with the Weather Research and Forecasting (WRF) model and the MITGCM ocean model coupled using the ESMF (Earth System Modeling Framework) coupling framework is developed to resolve mesoscale air-sea feedbacks. The regional coupled model allows oceanic mixed layer heat and momentum to interact with the atmospheric boundary layer dynamics at the mesoscale and submesoscale spatiotemporal regimes, thus leading to feedbacks which are otherwise not resolved in coarse resolution global coupled forecasting systems or regional uncoupled forecasting systems. The model is tested in two scenarios in the mesoscale eddy rich Red Sea and Western Indian Ocean region as well as mesoscale eddies and fronts of the California Current System. Recent studies show evidence for air-sea interactions involving the oceanic mesoscale in these two regions which can enhance predictability on sub seasonal timescale. We will present results from this newly developed regional coupled ocean-atmosphere model for forecasts over the Red Sea region as well as the California Current region. The forecasts will be validated against insitu observations in the region as well as reanalysis fields.

Quasi-static time-series simulation using OpenDSS in IEEE distribution feeder model with high PV penetration and its impact on solar forecasting

NASA Astrophysics Data System (ADS)

Mohammed, Touseef Ahmed Faisal

Since 2000, renewable electricity installations in the United States (excluding hydropower) have more than tripled. Renewable electricity has grown at a compounded annual average of nearly 14% per year from 2000-2010. Wind, Concentrated Solar Power (CSP) and solar Photo Voltaic (PV) are the fastest growing renewable energy sectors. In 2010 in the U.S., solar PV grew over 71% and CSP grew by 18% from the previous year. Globally renewable electricity installations have more than quadrupled from 2000-2010. Solar PV generation grew by a factor of more than 28 between 2000 and 2010. The amount of CSP and solar PV installations are increasing on the distribution grid. These PV installations transmit electrical current from the load centers to the generating stations. But the transmission and distribution grid have been designed for uni-directional flow of electrical energy from generating stations to load centers. This causes imbalances in voltage and switchgear of the electrical circuitry. With the continuous rise in PV installations, analysis of voltage profile and penetration levels remain an active area of research. Standard distributed photovoltaic (PV) generators represented in simulation studies do not reflect the exact location and variability properties such as distance between interconnection points to substations, voltage regulators, solar irradiance and other environmental factors. Quasi-Static simulations assist in peak load planning hour and day ahead as it gives a time sequence analysis to help in generation allocation. Simulation models can be daily, hourly or yearly depending on duty cycle and dynamics of the system. High penetration of PV into the power grid changes the voltage profile and power flow dynamically in the distribution circuits due to the inherent variability of PV. There are a number of modeling and simulations tools available for the study of such high penetration PV scenarios. This thesis will specifically utilize OpenDSS, a open source Distribution System Simulator developed by Electric Power Research Institute, to simulate grid voltage profile with a large scale PV system under quasi-static time series considering variations of PV output in seconds, minutes, and the average daily load variations. A 13 bus IEEE distribution feeder model is utilized with distributed residential and commercial scale PV at different buses for simulation studies. Time series simulations are discussed for various modes of operation considering dynamic PV penetration at different time periods in a day. In addition, this thesis demonstrates simulations taking into account the presence of moving cloud for solar forecasting studies.

Forecasting Influenza Epidemics in Hong Kong.

PubMed

Yang, Wan; Cowling, Benjamin J; Lau, Eric H Y; Shaman, Jeffrey

2015-07-01

Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.

Forecasting Influenza Epidemics in Hong Kong

PubMed Central

Yang, Wan; Cowling, Benjamin J.; Lau, Eric H. Y.; Shaman, Jeffrey

2015-01-01

Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions. PMID:26226185

Impacts of Climate Change on Electricity Consumption in Baden-Wuerttemberg

NASA Astrophysics Data System (ADS)

Mimler, S.

2009-04-01

Changes in electricity consumption due to changes in mean air temperatures were examined for the German federal state Baden-Wuerttemberg. Unlike in most recent studies on future electricity demand variations due to climate change, other load influencing factors like the economic, technological and demographic situation were fixed to the state of 2006. This allows isolating the climate change effect on electricity demand. The analysis was realised in two major steps. Firstly, an electricity forecast model based on multiple regressions was estimated on the region of Baden-Wuerttemberg by using historical load and temperature data. The estimation of the forecast model provides information on the temperature sensitivity of electricity demand in the given region. The overall heating and cooling gradients are estimated with -59 and 84 MW / °C respectively. These results already point out a low temperature sensitivity of demand in the region of Baden-Wuerttemberg mostly due to a low share of households equipped with electric heating and air conditioning systems. Secondly, near surface air temperature data of the regional climate model REMO [1] was used to simulate load curves for the control period 1971 to 2000 and for three future scenarios 2006 to 2035, 2036 to 2065 and 2066 to 2095. The results show that the overall load decreases throughout all future scenario periods in comparison to the control period. This is due to a higher decrease in heating than increase in cooling load. Nevertheless, the weather dependent part in Baden-Wuerttemberg loads only accounts for 0.05 % of the average load level. Within this weather dependent part, the heating load decreases are highest in June to September concentrated on the day times evening and afternoon. The cooling period broadens from May to September in the control period to April to October by 2095. The highest relative increases occur in October. Regarding day times, the increase in cooling load is concentrated on afternoons, evenings and nights. [1] Jacob, D. (2005a), "REMO A1B Scenario run, UBA project, 0.088 degree resolution, run no.006211, 1H data", World Data Center for Climate, CERA-DB "REMO_UBA_A1B_1_R006211_1H", http://cera-www.dkrz.de/WDCC/ui/Compact.jsp? acronym=REMO_UBA_A1B_1_R006211_1H Jacob, D. (2005b), "REMO climate of the 20th century run, UBA project, 0.088 degree resolution, run no. 006210, 1H data", World Data Center for Climate, CERA-DB "REMO_UBA_C20_1_R006210_1H", http://cera-www.dkrz.de/WDCC/ui/Compact. jsp?acronym=REMO_UBA_C20_1_R006210_1H

Operating health analysis of electric power systems

NASA Astrophysics Data System (ADS)

Fotuhi-Firuzabad, Mahmud

The required level of operating reserve to be maintained by an electric power system can be determined using both deterministic and probabilistic techniques. Despite the obvious disadvantages of deterministic approaches there is still considerable reluctance to apply probabilistic techniques due to the difficulty of interpreting a single numerical risk index and the lack of sufficient information provided by a single index. A practical way to overcome difficulties is to embed deterministic considerations in the probabilistic indices in order to monitor the system well-being. The system well-being can be designated as healthy, marginal and at risk. The concept of system well-being is examined and extended in this thesis to cover the overall area of operating reserve assessment. Operating reserve evaluation involves the two distinctly different aspects of unit commitment and the dispatch of the committed units. Unit commitment health analysis involves the determination of which unit should be committed to satisfy the operating criteria. The concepts developed for unit commitment health, margin and risk are extended in this thesis to evaluate the response well-being of a generating system. A procedure is presented to determine the optimum dispatch of the committed units to satisfy the response criteria. The impact on the response wellbeing being of variations in the margin time, required regulating margin and load forecast uncertainty are illustrated. The effects on the response well-being of rapid start units, interruptible loads and postponable outages are also illustrated. System well-being is, in general, greatly improved by interconnection with other power systems. The well-being concepts are extended to evaluate the spinning reserve requirements in interconnected systems. The interconnected system unit commitment problem is decomposed into two subproblems in which unit scheduling is performed in each isolated system followed by interconnected system evaluation. A procedure is illustrated to determine the well-being indices of the overall interconnected system. Under normal operating conditions, the system may also be able to carry a limited amount of interruptible load on top of its firm load without violating the operating criterion. An energy based approach is presented to determine the optimum interruptible load carrying capability in both the isolated and interconnected systems. Composite system spinning reserve assessment and composite system well-being are also examined in this research work. The impacts on the composite well-being of operating reserve considerations such as stand-by units, interruptible loads and the physical locations of these resources are illustrated. It is expected that the well-being framework and the concepts developed in this research work will prove extremely useful in the new competitive utility environment.

Self-Organizing Maps-based ocean currents forecasting system.

PubMed

Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

2016-03-16

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.

Self-Organizing Maps-based ocean currents forecasting system

PubMed Central

Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

2016-01-01

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training. PMID:26979129

Action-based flood forecasting for triggering humanitarian action

NASA Astrophysics Data System (ADS)

Coughlan de Perez, Erin; van den Hurk, Bart; van Aalst, Maarten K.; Amuron, Irene; Bamanya, Deus; Hauser, Tristan; Jongma, Brenden; Lopez, Ana; Mason, Simon; Mendler de Suarez, Janot; Pappenberger, Florian; Rueth, Alexandra; Stephens, Elisabeth; Suarez, Pablo; Wagemaker, Jurjen; Zsoter, Ervin

2016-09-01

Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.

Seasonal drought ensemble predictions based on multiple climate models in the upper Han River Basin, China

NASA Astrophysics Data System (ADS)

Ma, Feng; Ye, Aizhong; Duan, Qingyun

2017-03-01

An experimental seasonal drought forecasting system is developed based on 29-year (1982-2010) seasonal meteorological hindcasts generated by the climate models from the North American Multi-Model Ensemble (NMME) project. This system made use of a bias correction and spatial downscaling method, and a distributed time-variant gain model (DTVGM) hydrologic model. DTVGM was calibrated using observed daily hydrological data and its streamflow simulations achieved Nash-Sutcliffe efficiency values of 0.727 and 0.724 during calibration (1978-1995) and validation (1996-2005) periods, respectively, at the Danjiangkou reservoir station. The experimental seasonal drought forecasting system (known as NMME-DTVGM) is used to generate seasonal drought forecasts. The forecasts were evaluated against the reference forecasts (i.e., persistence forecast and climatological forecast). The NMME-DTVGM drought forecasts have higher detectability and accuracy and lower false alarm rate than the reference forecasts at different lead times (from 1 to 4 months) during the cold-dry season. No apparent advantage is shown in drought predictions during spring and summer seasons because of a long memory of the initial conditions in spring and a lower predictive skill for precipitation in summer. Overall, the NMME-based seasonal drought forecasting system has meaningful skill in predicting drought several months in advance, which can provide critical information for drought preparedness and response planning as well as the sustainable practice of water resource conservation over the basin.

Visualization of ocean forecast in BYTHOS

NASA Astrophysics Data System (ADS)

Zhuk, E.; Zodiatis, G.; Nikolaidis, A.; Stylianou, S.; Karaolia, A.

2016-08-01

The Cyprus Oceanography Center has been constantly searching for new ideas for developing and implementing innovative methods and new developments concerning the use of Information Systems in Oceanography, to suit both the Center's monitoring and forecasting products. Within the frame of this scope two major online managing and visualizing data systems have been developed and utilized, those of CYCOFOS and BYTHOS. The Cyprus Coastal Ocean Forecasting and Observing System - CYCOFOS provides a variety of operational predictions such as ultra high, high and medium resolution ocean forecasts in the Levantine Basin, offshore and coastal sea state forecasts in the Mediterranean and Black Sea, tide forecasting in the Mediterranean, ocean remote sensing in the Eastern Mediterranean and coastal and offshore monitoring. As a rich internet application, BYTHOS enables scientists to search, visualize and download oceanographic data online and in real time. The recent improving of BYTHOS system is the extension with access and visualization of CYCOFOS data and overlay forecast fields and observing data. The CYCOFOS data are stored at OPENDAP Server in netCDF format. To search, process and visualize it the php and python scripts were developed. Data visualization is achieved through Mapserver. The BYTHOS forecast access interface allows to search necessary forecasting field by recognizing type, parameter, region, level and time. Also it provides opportunity to overlay different forecast and observing data that can be used for complex analyze of sea basin aspects.

Assimilation of GMS-5 satellite winds using nudging method with MM5

NASA Astrophysics Data System (ADS)

Gao, Shanhong; Wu, Zengmao; Yang, Bo

2006-09-01

With the aid of Meteorological Information Composite and Processing System (MICAPS), satellite wind vectors derived from the Geostationary Meteorological Statellite-5 (GMS-5) and retrieved by National Satellite Meteorology Center of China (NSMC) can be obtained. Based on the nudging method built in the fifth-generation Mesoscale Model (MM5) of Pennsylvania State University and National Center for Atmospheric Research, a data preprocessor is developed to convert these satellite wind vectors to those with specified format required in MM5. To examine the data preprocessor and evaluate the impact of satellite winds from GMS-5 on MM5 simulations, a series of numerical experimental forecasts consisting of four typhoon cases in 2002 are designed and implemented. The results show that the preprocessor can process satellite winds smoothly and MM5 model runs successfully with a little extra computational load during ingesting these winds, and that assimilation of satellite winds by MM5 nudging method can obviously improve typhoon track forecast but contributes a little to typhoon intensity forecast. The impact of the satellite winds depends heavily upon whether the typhoon bogussing scheme in MM5 was turned on or not. The data preprocessor developed in this paper not only can treat GMS-5 satellite winds but also has capability with little modification to process derived winds from other geostationary satellites.

Fertiliser management effects on dissolved inorganic nitrogen in runoff from Australian sugarcane farms.

PubMed

Fraser, Grant; Rohde, Ken; Silburn, Mark

2017-08-01

Dissolved inorganic nitrogen (DIN) movement from Australian sugarcane farms is believed to be a major cause of crown-of-thorns starfish outbreaks which have reduced the Great Barrier Reef coral cover by ~21% (1985-2012). We develop a daily model of DIN concentration in runoff based on >200 field monitored runoff events. Runoff DIN concentrations were related to nitrogen fertiliser application rates and decreased after application with time and cumulative rainfall. Runoff after liquid fertiliser applications had higher initial DIN concentrations, though these concentrations diminished more rapidly in comparison to granular fertiliser applications. The model was validated using an independent field dataset and provided reasonable estimates of runoff DIN concentrations based on a number of modelling efficiency score results. The runoff DIN concentration model was combined with a water balance cropping model to investigate temporal aspects of sugarcane fertiliser management. Nitrogen fertiliser application in December (start of wet season) had the highest risk of DIN movement, and this was further exacerbated in years with a climate forecast for 'wet' seasonal conditions. The potential utility of a climate forecasting system to predict forthcoming wet months and hence DIN loss risk is demonstrated. Earlier fertiliser application or reducing fertiliser application rates in seasons with a wet climate forecast may markedly reduce runoff DIN loads; however, it is recommended that these findings be tested at a broader scale.

Ocean dynamics during the passage of Xynthia storm recorded by GPS

NASA Astrophysics Data System (ADS)

Nicolas, Joëlle; Ferenc, Marcell; Li, Zhao; van Dam, Tonie; Polidori, Laurent

2014-05-01

When computing the effect of atmospheric loading on geodetic coordinates, we must assign the response of the ocean to pressure loading. A pure inverted barometer and a solid Earth ocean response to pressure loading define the extremes of the response. At periods longer than a few days, the inverted barometer response is sufficient (Wunsch and Stammer, 1997). However, how does the ocean respond to fast moving storms? In this study we investigate the effect of a violent storm that progressed over Western Europe between the 27th of February and the 1st of March 2010 on sub-daily vertical GPS (Global Positioning System) position time series of the French GNSS permanent network (RGP). Xynthia was a huge low-pressure system (pressure drop of 40 mbar and a storm surge of 1.4 m (at La Rochelle tide gauge)) that crossed France from the southwest to the northeast over the course of about 20 hours. We study the different behaviour of the coastal and inland sites based on the comparison of the estimated 6-hourly stand-alone GPS position time series (GINS-PC software) with the local pressure and the predicted atmospheric pressure loading time series derived from the high resolution Modern-Era Retrospective Analysis for Research and Applications (NASA MERRA) and also the European Centre for Medium-Range Weather Forecasts (ECMWF) global dataset. We model the predicted displacements using the inverse barometer (IB) and the non-IB ocean response cases as endpoints. Predicted loading effects due to the atmospheric pressure and IB ocean reach up to 1.0, 1.3 and 13.7 mm for the east, north and up components, respectively. Then we attempt to use the GPS vertical surface displacements, the surface pressure, and tide gauge data (SONEL) to identify the true ocean dynamics on the continental shelf during the passage of this fast moving system. Keywords: GPS, GINS-PC, Xynthia, ocean dynamics, atmospheric pressure loading, deformation

Development of an Adaptable Display and Diagnostic System for the Evaluation of Tropical Cyclone Forecasts

NASA Astrophysics Data System (ADS)

Kucera, P. A.; Burek, T.; Halley-Gotway, J.

2015-12-01

NCAR's Joint Numerical Testbed Program (JNTP) focuses on the evaluation of experimental forecasts of tropical cyclones (TCs) with the goal of developing new research tools and diagnostic evaluation methods that can be transitioned to operations. Recent activities include the development of new TC forecast verification methods and the development of an adaptable TC display and diagnostic system. The next generation display and diagnostic system is being developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and broader TC research community. The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models. The system is built upon modern and flexible technology that includes OpenLayers Mapping tools that are platform independent. The forecast track and intensity along with associated observed track information are stored in an efficient MySQL database. The system provides easy-to-use interactive display system, and provides diagnostic tools to examine forecast track stratified by intensity. Consensus forecasts can be computed and displayed interactively. The system is designed to display information for both real-time and for historical TC cyclones. The display configurations are easily adaptable to meet the needs of the end-user preferences. Ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks, development and implementation of additional methods to stratify and compute consensus hurricane track and intensity forecasts, and improved graphical display tools. The display is also being enhanced to incorporate gridded forecast, satellite, and sea surface temperature fields. The presentation will provide an overview of the display and diagnostic system development and demonstration of the current capabilities.

The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

NASA Astrophysics Data System (ADS)

Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

2010-09-01

Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events, has become evident. However, despite the demonstrated advantages, worldwide the incorporation of HEPS in operational flood forecasting is still limited. The applicability of HEPS for smaller river basins was tested in MAP D-Phase, an acronym for "Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region" which was launched in 2005 as a Forecast Demonstration Project of World Weather Research Programme of WMO, and entered a pre-operational and still active testing phase in 2007. In Europe, a comparatively high number of EPS driven systems for medium-large rivers exist. National flood forecasting centres of Sweden, Finland and the Netherlands, have already implemented HEPS in their operational forecasting chain, while in other countries including France, Germany, Czech Republic and Hungary, hybrids or experimental chains have been installed. As an example of HEPS, the European Flood Alert System (EFAS) is being presented. EFAS provides medium-range probabilistic flood forecasting information for large trans-national river basins. It incorporates multiple sets of weather forecast including different types of EPS and deterministic forecasts from different providers. EFAS products are evaluated and visualised as exceedance of critical levels only - both in forms of maps and time series. Different sources of uncertainty and its impact on the flood forecasting performance for every grid cell has been tested offline but not yet incorporated operationally into the forecasting chain for computational reasons. However, at stations where real-time discharges are available, a hydrological uncertainty processor is being applied to estimate the total predictive uncertainty from the hydrological and input uncertainties. Research on long-term EFAS results has shown the need for complementing statistical analysis with case studies for which examples will be shown.

Traffic flow forecasting for intelligent transportation systems.

DOT National Transportation Integrated Search

1995-01-01

The capability to forecast traffic volume in an operational setting has been identified as a critical need for intelligent transportation systems (ITS). In particular, traffic volume forecasts will directly support proactive traffic control and accur...

Evaluation of precipitation forecasts from 3D-Var and hybrid GSI-based system during Indian summer monsoon 2015

NASA Astrophysics Data System (ADS)

Singh, Sanjeev Kumar; Prasad, V. S.

2018-02-01

This paper presents a systematic investigation of medium-range rainfall forecasts from two versions of the National Centre for Medium Range Weather Forecasting (NCMRWF)-Global Forecast System based on three-dimensional variational (3D-Var) and hybrid analysis system namely, NGFS and HNGFS, respectively, during Indian summer monsoon (June-September) 2015. The NGFS uses gridpoint statistical interpolation (GSI) 3D-Var data assimilation system, whereas HNGFS uses hybrid 3D ensemble-variational scheme. The analysis includes the evaluation of rainfall fields and comparisons of rainfall using statistical score such as mean precipitation, bias, correlation coefficient, root mean square error and forecast improvement factor. In addition to these, categorical scores like Peirce skill score and bias score are also computed to describe particular aspects of forecasts performance. The comparison results of mean precipitation reveal that both the versions of model produced similar large-scale feature of Indian summer monsoon rainfall for day-1 through day-5 forecasts. The inclusion of fully flow-dependent background error covariance significantly improved the wet biases in HNGFS over the Indian Ocean. The forecast improvement factor and Peirce skill score in the HNGFS have also found better than NGFS for day-1 through day-5 forecasts.

Assessment of Forecast Sensitivity to Observation and Its Application to Satellite Radiances

NASA Astrophysics Data System (ADS)

Ide, K.

2017-12-01

The Forecast sensitivity to observation provides practical and useful metric for the assessment of observation impact without conducting computationally intensive data denial experiments. Quite often complex data assimilation systems use a simplified version of the forecast sensitivity formulation based on ensembles. In this talk, we first present the comparison of forecast sensitivity for 4DVar, Hybrid-4DEnVar, and 4DEnKF with or without such simplifications using a highly nonlinear model. We then present the results of ensemble forecast sensitivity to satellite radiance observations for Hybrid-4DEnVart using NOAA's Global Forecast System.

Application of MODIS-Derived Active Fire Radiative Energy to Fire Disaster and Smoke Pollution Monitoring

NASA Technical Reports Server (NTRS)

Ichoku, Charles; Kaufman, Yoram J.; Hao, Wei Min; Habib, Shahid

2004-01-01

The radiative energy emitted by large fires and the corresponding smoke aerosol loading are simultaneously measured from the MODIS sensor from both the Terra and Aqua satellites. Quantitative relationships between the rates of emission of fire radiative energy and smoke are being developed for different fire-prone regions of the globe. Preliminary results are presented. When fully developed, the system will enable the use of MODIS direct broadcast fire data for near real-time monitoring of fire strength and smoke emission as well as forecasting of fire progression and smoke dispersion, several hours to a few days in advance.

The meta-Gaussian Bayesian Processor of forecasts and associated preliminary experiments

NASA Astrophysics Data System (ADS)

Chen, Fajing; Jiao, Meiyan; Chen, Jing

2013-04-01

Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.

How do I know if I’ve improved my continental scale flood early warning system?

NASA Astrophysics Data System (ADS)

Cloke, Hannah L.; Pappenberger, Florian; Smith, Paul J.; Wetterhall, Fredrik

2017-04-01

Flood early warning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood early warning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood early warning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value. The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to early warning systems.

NCEP Data Products

Science.gov Websites

Image of NCEP Logo WHERE AMERICA'S CLIMATE AND WEATHER SERVICES BEGIN Inventory of Data Products on Generated Products Image of horizontal rule Global Forecast System (GFS) GFS Ensemble Forecast System (GEFS of horizontal rule External Products Image of horizontal rule Canadian Ensemble Forecast System

Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

NASA Astrophysics Data System (ADS)

Shedd, R.; Reed, S. M.; Porter, J. H.

2015-12-01

The National Weather Service (NWS) has been working for several years on the development of the Hydrologic Ensemble Forecast System (HEFS). The objective of HEFS is to provide ensemble river forecasts incorporating the best precipitation and temperature forcings at any specific time horizon. For the current implementation, this includes the Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFSv2). One of the core partners that has been working with the NWS since the beginning of the development phase of HEFS is the New York City Department of Environmental Protection (NYCDEP) which is responsible for the complex water supply system for New York City. The water supply system involves a network of reservoirs in both the Delaware and Hudson River basins. At the same time that the NWS was developing HEFS, NYCDEP was working on enhancing the operations of their water supply reservoirs through the development of a new Operations Support Tool (OST). OST is designed to guide reservoir system operations to ensure an adequate supply of high-quality drinking water for the city, as well as to meet secondary objectives for reaches downstream of the reservoirs assuming the primary water supply goals can be met. These secondary objectives include fisheries and ecosystem support, enhanced peak flow attenuation beyond that provided natively by the reservoirs, salt front management, and water supply for other cities. Since January 2014, the NWS Northeast and Middle Atlantic River Forecast Centers have provided daily one year forecasts from HEFS to NYCDEP. OST ingests these forecasts, couples them with near-real-time environmental and reservoir system data, and drives models of the water supply system. The input of ensemble forecasts results in an ensemble of model output, from which information on the range and likelihood of possible future system states can be extracted. This type of probabilistic information provides system managers with additional information not available from deterministic forecasts and allows managers to better assess risk, and provides greater context for decision-making than has been available in the past. HEFS has allowed NYCDEP water supply managers to make better decisions on reservoir operations than they likely would have in the past, using only deterministic forecasts.

The Copernicus Atmosphere Monitoring Service: facilitating the prediction of air quality from global to local scales

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The regional forecasts are produced by an ensemble of seven operational European air quality models that take their boundary conditions from the global system and provide an ensemble median with ensemble spread as their main output. Both the global and regional forecasting systems are feeding their output into air quality models on a variety of scales in various parts of the world. We will introduce the CAMS service chain and provide illustrations of its use in downstream applications. Both the usage of the daily forecasts and the usage of global and regional reanalyses will be addressed.

Vertical distribution of Saharan dust over Rome (Italy): Comparison between 3-year model predictions and lidar soundings

NASA Astrophysics Data System (ADS)

Kishcha, P.; Barnaba, F.; Gobbi, G. P.; Alpert, P.; Shtivelman, A.; Krichak, S. O.; Joseph, J. H.

2005-03-01

Mineral dust particles loaded into the atmosphere from the Sahara desert represent one major factor affecting the Earth's radiative budget. Regular model-based forecasts of 3-D dust fields can be used in order to determine the dust radiative effect in climate models, in spite of the large gaps in observations of dust vertical profiles. In this study, dust forecasts by the Tel Aviv University (TAU) dust prediction system were compared to lidar observations to better evaluate the model's capabilities. The TAU dust model was initially developed at the University of Athens and later modified at Tel Aviv University. Dust forecasts are initialized with the aid of the Total Ozone Mapping Spectrometer aerosol index (TOMS AI) measurements. The lidar soundings employed were collected at the outskirts of Rome, Italy (41.84°N, 12.64°E) during the high-dust activity season from March to June of the years 2001, 2002, and 2003. The lidar vertical profiles collected in the presence of dust were used for obtaining statistically significant reference parameters of dust layers over Rome and for model versus lidar comparison. The Barnaba and Gobbi (2001) approach was used in the current study to derive height-resolved dust volumes from lidar measurements of backscatter. Close inspection of the juxtaposed vertical profiles, obtained from lidar and model data near Rome, indicates that the majority (67%) of the cases under investigation can be classified as good or acceptable forecasts of the dust vertical distribution. A more quantitative comparison shows that the model predictions are mainly accurate in the middle part of dust layers. This is supported by high correlation (0.85) between lidar and model data for forecast dust volumes greater than the threshold of 1 × 10-12 cm3/cm3. In general, however, the model tends to underestimate the lidar-derived dust volume profiles. The effect of clouds in the TOMS detection of AI is supposed to be the main factor responsible for this effect. Moreover, some model assumptions on dust sources and particle size and the accuracy of model-simulated meteorological parameters are also likely to affect the dust forecast quality.

Adequacy assessment of composite generation and transmission systems incorporating wind energy conversion systems

NASA Astrophysics Data System (ADS)

Gao, Yi

The development and utilization of wind energy for satisfying electrical demand has received considerable attention in recent years due to its tremendous environmental, social and economic benefits, together with public support and government incentives. Electric power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of wind energy facilities therefore affect power system reliability in a different manner than those of conventional systems. The reliability impact of such a highly variable energy source is an important aspect that must be assessed when the wind power penetration is significant. The focus of the research described in this thesis is on the utilization of state sampling Monte Carlo simulation in wind integrated bulk electric system reliability analysis and the application of these concepts in system planning and decision making. Load forecast uncertainty is an important factor in long range planning and system development. This thesis describes two approximate approaches developed to reduce the number of steps in a load duration curve which includes load forecast uncertainty, and to provide reasonably accurate generating and bulk system reliability index predictions. The developed approaches are illustrated by application to two composite test systems. A method of generating correlated random numbers with uniform distributions and a specified correlation coefficient in the state sampling method is proposed and used to conduct adequacy assessment in generating systems and in bulk electric systems containing correlated wind farms in this thesis. The studies described show that it is possible to use the state sampling Monte Carlo simulation technique to quantitatively assess the reliability implications associated with adding wind power to a composite generation and transmission system including the effects of multiple correlated wind sites. This is an important development as it permits correlated wind farms to be incorporated in large practical system studies without requiring excessive increases in computer solution time. The procedures described in this thesis for creating monthly and seasonal wind farm models should prove useful in situations where time period models are required to incorporate scheduled maintenance of generation and transmission facilities. There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct bulk power system planning. A relatively new approach that incorporates deterministic and probabilistic considerations in a single risk assessment framework has been designated as the joint deterministic-probabilistic approach. The research work described in this thesis illustrates that the joint deterministic-probabilistic approach can be effectively used to integrate wind power in bulk electric system planning. The studies described in this thesis show that the application of the joint deterministic-probabilistic method provides more stringent results for a system with wind power than the traditional deterministic N-1 method because the joint deterministic-probabilistic technique is driven by the deterministic N-1 criterion with an added probabilistic perspective which recognizes the power output characteristics of a wind turbine generator.

Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.

Commercial equipment loads: End-Use Load and Consumer Assessment Program (ELCAP)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pratt, R.G.; Williamson, M.A.; Richman, E.E.

1990-07-01

The Office of Energy Resources of the Bonneville Power Administration is generally responsible for the agency's power and conservation resource planning. As associated responsibility which supports a variety of office functions is the analysis of historical trends in and determinants of energy consumption. The Office of Energy Resources' End-Use Research Section operates a comprehensive data collection program to provide pertinent information to support demand-side planning, load forecasting, and demand-side program development and delivery. Part of this on-going program is known as the End-Use Load and Consumer Assessment Program (ELCAP), an effort designed to collect electricity usage data through direct monitoringmore » of end-use loads in buildings. This program is conducted for Bonneville by the Pacific Northwest Laboratory. This report provides detailed information on electricity consumption of miscellaneous equipment from the commercial portion of ELCAP. Miscellaneous equipment includes all commercial end-uses except heating, ventilating, air conditioning, and central lighting systems. Some examples of end-uses covered in this report are office equipment, computers, task lighting, refrigeration, and food preparation. Electricity consumption estimates, in kilowatt-hours per square food per year, are provided for each end-use by building type. The following types of buildings are covered: office, retail, restaurant, grocery, warehouse, school, university, and hotel/motel. 6 refs., 35 figs., 12 tabs.« less

Integrating observation and statistical forecasts over sub-Saharan Africa to support Famine Early Warning

USGS Publications Warehouse

Funk, Chris; Verdin, James P.; Husak, Gregory

2007-01-01

Famine early warning in Africa presents unique challenges and rewards. Hydrologic extremes must be tracked and anticipated over complex and changing climate regimes. The successful anticipation and interpretation of hydrologic shocks can initiate effective government response, saving lives and softening the impacts of droughts and floods. While both monitoring and forecast technologies continue to advance, discontinuities between monitoring and forecast systems inhibit effective decision making. Monitoring systems typically rely on high resolution satellite remote-sensed normalized difference vegetation index (NDVI) and rainfall imagery. Forecast systems provide information on a variety of scales and formats. Non-meteorologists are often unable or unwilling to connect the dots between these disparate sources of information. To mitigate these problem researchers at UCSB's Climate Hazard Group, NASA GIMMS and USGS/EROS are implementing a NASA-funded integrated decision support system that combines the monitoring of precipitation and NDVI with statistical one-to-three month forecasts. We present the monitoring/forecast system, assess its accuracy, and demonstrate its application in food insecure sub-Saharan Africa.

An Approach to Assess Observation Impact Based on Observation-Minus-Forecast Residuals

NASA Technical Reports Server (NTRS)

Todling, Ricardo

2009-01-01

Langland and Baker (2004) introduced an approach to assess the impact of observations on the forecasts. In that, a state-space aspect of the forecast is defined and a procedure is derived that relates changes in the aspect with changes in the initial conditions associated with the assimilation of observations) ultimately providing information about the impact of individual observations on the forecast. Some features of the approach are to be noted. The typical choice of forecast aspect employed in related works is rather arbitrary and leads to an incomplete assessment of the observing system. Furthermore, the state-space forecast aspect requires availability of a verification state that should ideally be uncorrelated with the forecast but in practice is not. Lastly, the approach involves the adjoint operator of the entire data assimilation system and as such it is constrained by the validity of this operator. In this presentation, an observation-space metric is used that, for a relatively time-homogeneous observing system, allows inferring observation impact on the forecast without some of the limitations above. Specifically, using observation-minus-forecast residuals leads to an approach with the following features: (i) it suggests a rather natural choice of forecast aspect, directly linked to the analysis system and providing full assessment of the observations; (ii) it naturally avoids introducing undesirable correlations in the forecast aspect by verifying against the observations; and (iii) it does not involve linearization and use of adjoints; therefore being applicable to any length of forecast. The state and observation-space approaches might be complementary to some degree, and involve different limitations and complexities. Illustrations are given using the NASA GEOS-5 data.

Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

NASA Astrophysics Data System (ADS)

Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin

2015-12-01

In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

The Rise of Complexity in Flood Forecasting: Opportunities, Challenges and Tradeoffs

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, M. P.; Nijssen, B.

2017-12-01

Operational flood forecasting is currently undergoing a major transformation. Most national flood forecasting services have relied for decades on lumped, highly calibrated conceptual hydrological models running on local office computing resources, providing deterministic streamflow predictions at gauged river locations that are important to stakeholders and emergency managers. A variety of recent technological advances now make it possible to run complex, high-to-hyper-resolution models for operational hydrologic prediction over large domains, and the US National Weather Service is now attempting to use hyper-resolution models to create new forecast services and products. Yet other `increased-complexity' forecasting strategies also exist that pursue different tradeoffs between model complexity (i.e., spatial resolution, physics) and streamflow forecast system objectives. There is currently a pressing need for a greater understanding in the hydrology community of the opportunities, challenges and tradeoffs associated with these different forecasting approaches, and for a greater participation by the hydrology community in evaluating, guiding and implementing these approaches. Intermediate-resolution forecast systems, for instance, use distributed land surface model (LSM) physics but retain the agility to deploy ensemble methods (including hydrologic data assimilation and hindcast-based post-processing). Fully coupled numerical weather prediction (NWP) systems, another example, use still coarser LSMs to produce ensemble streamflow predictions either at the model scale or after sub-grid scale runoff routing. Based on the direct experience of the authors and colleagues in research and operational forecasting, this presentation describes examples of different streamflow forecast paradigms, from the traditional to the recent hyper-resolution, to illustrate the range of choices facing forecast system developers. We also discuss the degree to which the strengths and weaknesses of each strategy map onto the requirements for different types of forecasting services (e.g., flash flooding, river flooding, seasonal water supply prediction).

Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert

2017-11-01

Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias-correction method should be further investigated to remedy this weakness and take more advantage of the ensemble forecasts produced by the climate model. Overall, in this study, bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting for lead times up to 1 month. They could also complement ESP for longer lead times.

Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Optis, Michael; Scott, George N.; Draxl, Caroline

The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present.more » Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.« less

Damage prognosis: the future of structural health monitoring.

PubMed

Farrar, Charles R; Lieven, Nick A J

2007-02-15

This paper concludes the theme issue on structural health monitoring (SHM) by discussing the concept of damage prognosis (DP). DP attempts to forecast system performance by assessing the current damage state of the system (i.e. SHM), estimating the future loading environments for that system, and predicting through simulation and past experience the remaining useful life of the system. The successful development of a DP capability will require the further development and integration of many technology areas including both measurement/processing/telemetry hardware and a variety of deterministic and probabilistic predictive modelling capabilities, as well as the ability to quantify the uncertainty in these predictions. The multidisciplinary and challenging nature of the DP problem, its current embryonic state of development, and its tremendous potential for life-safety and economic benefits qualify DP as a 'grand challenge' problem for engineers in the twenty-first century.

Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables

DOE Office of Scientific and Technical Information (OSTI.GOV)

DallAnese, Emiliano; Baker, Kyri; Summers, Tyler

This paper focuses on distribution systems featuring renewable energy sources (RESs) and energy storage systems, and presents an AC optimal power flow (OPF) approach to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads. The proposed method hinges on a chance-constrained AC OPF formulation where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability. A computationally more affordable convex reformulation is developed by resorting to suitable linear approximations of the AC power-flow equations as well as convex approximations of the chance constraints. The approximate chance constraints provide conservative bounds that hold for arbitrarymore » distributions of the forecasting errors. An adaptive strategy is then obtained by embedding the proposed AC OPF task into a model predictive control framework. Finally, a distributed solver is developed to strategically distribute the solution of the optimization problems across utility and customers.« less

VERIFICATION OF SURFACE LAYER OZONE FORECASTS IN THE NOAA/EPA AIR QUALITY FORECAST SYSTEM IN DIFFERENT REGIONS UNDER DIFFERENT SYNOPTIC SCENARIOS

EPA Science Inventory

An air quality forecast (AQF) system has been established at NOAA/NCEP since 2003 as a collaborative effort of NOAA and EPA. The system is based on NCEP's Eta mesoscale meteorological model and EPA's CMAQ air quality model (Davidson et al, 2004). The vision behind this system is ...

Final research findings on traffic-load forecasting using weigh-in-motion data

DOT National Transportation Integrated Search

1998-09-01

The overall objective of Project 7-987 was to develop a long-range pavement rehabilitation plan for a segment of US 59, a four-lane divided principal arterial highway in TxDOT's Lufkin District. To identify feasible pavement : structures, test sectio...

7 CFR 1710.300 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... the forecast, including the methodology used to project loads, rates, revenue, power costs, operating expenses, plant additions, and other factors having a material effect on the balance sheet and on financial... regional office will consult with the Power Supply Division in the case of generation projects for...

Operational seasonal forecasting of crop performance.

PubMed

Stone, Roger C; Meinke, Holger

2005-11-29

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.

Operational seasonal forecasting of crop performance

PubMed Central

Stone, Roger C; Meinke, Holger

2005-01-01

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097

Satellite based Ocean Forecasting, the SOFT project

NASA Astrophysics Data System (ADS)

Stemmann, L.; Tintoré, J.; Moneris, S.

2003-04-01

The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.

Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madankan, R.; Pouget, S.; Singla, P., E-mail: psingla@buffalo.edu

Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions – height, profile of particle location, volcanic vent parameters – are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This papermore » presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain source parameters, based on observed satellite imagery. The April 2010 eruption of the Eyjafjallajökull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14–16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.« less

Developing Environmental Scanning/Forecasting Systems To Augment Community College Planning.

ERIC Educational Resources Information Center

Morrison, James L.; Held, William G.

A description is provided of a conference session that was conducted to explore the structure and function of an environmental scanning/forecasting system that could be used in a community college to facilitate planning. Introductory comments argue that a college that establishes an environmental scanning and forecasting system is able to identify…

Flash-flood early warning using weather radar data: from nowcasting to forecasting

NASA Astrophysics Data System (ADS)

Liechti, Katharina; Panziera, Luca; Germann, Urs; Zappa, Massimiliano

2013-04-01

In our study we explore the limits of radar-based forecasting for hydrological runoff prediction. Two novel probabilistic radar-based forecasting chains for flash-flood early warning are investigated in three catchments in the Southern Swiss Alps and set in relation to deterministic discharge forecast for the same catchments. The first probabilistic radar-based forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second probabilistic forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialized with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 hours between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. We found a clear preference for the probabilistic approach. Discharge forecasts perform better when forced by NORA rather than by a persistent radar QPE for lead times up to eight hours and for all discharge thresholds analysed. The best results were, however, obtained with the REAL-C2 forecasting chain, which was also remarkably skilful even with the highest thresholds. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic forcing.

Flash-flood early warning using weather radar data: from nowcasting to forecasting

NASA Astrophysics Data System (ADS)

Liechti, K.; Panziera, L.; Germann, U.; Zappa, M.

2013-01-01

This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel probabilistic radar-based forecasting chains for flash-flood early warning are investigated in three catchments in the Southern Swiss Alps and set in relation to deterministic discharge forecast for the same catchments. The first probabilistic radar-based forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second probabilistic forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialized with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. We found a clear preference for the probabilistic approach. Discharge forecasts perform better when forced by NORA rather than by a persistent radar QPE for lead times up to eight hours and for all discharge thresholds analysed. The best results were, however, obtained with the REAL-C2 forecasting chain, which was also remarkably skilful even with the highest thresholds. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.

Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

2017-05-17

A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severemore » voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.« less

The Value of Humans in the Operational River Forecasting Enterprise

NASA Astrophysics Data System (ADS)

Pagano, T. C.

2012-04-01

The extent of human control over operational river forecasts, such as by adjusting model inputs and outputs, varies from nearly completely automated systems to those where forecasts are generated after discussion among a group of experts. Historical and realtime data availability, the complexity of hydrologic processes, forecast user needs, and forecasting institution support/resource availability (e.g. computing power, money for model maintenance) influence the character and effectiveness of operational forecasting systems. Automated data quality algorithms, if used at all, are typically very basic (e.g. checks for impossible values); substantial human effort is devoted to cleaning up forcing data using subjective methods. Similarly, although it is an active research topic, nearly all operational forecasting systems struggle to make quantitative use of Numerical Weather Prediction model-based precipitation forecasts, instead relying on the assessment of meteorologists. Conversely, while there is a strong tradition in meteorology of making raw model outputs available to forecast users via the Internet, this is rarely done in hydrology; Operational river forecasters express concerns about exposing users to raw guidance, due to the potential for misinterpretation and misuse. However, this limits the ability of users to build their confidence in operational products through their own value-added analyses. Forecasting agencies also struggle with provenance (i.e. documenting the production process and archiving the pieces that went into creating a forecast) although this is necessary for quantifying the benefits of human involvement in forecasting and diagnosing weak links in the forecasting chain. In hydrology, the space between model outputs and final operational products is nearly unstudied by the academic community, although some studies exist in other fields such as meteorology.

Regional early flood warning system: design and implementation

NASA Astrophysics Data System (ADS)

Chang, L. C.; Yang, S. N.; Kuo, C. L.; Wang, Y. F.

2017-12-01

This study proposes a prototype of the regional early flood inundation warning system in Tainan City, Taiwan. The AI technology is used to forecast multi-step-ahead regional flood inundation maps during storm events. The computing time is only few seconds that leads to real-time regional flood inundation forecasting. A database is built to organize data and information for building real-time forecasting models, maintaining the relations of forecasted points, and displaying forecasted results, while real-time data acquisition is another key task where the model requires immediately accessing rain gauge information to provide forecast services. All programs related database are constructed in Microsoft SQL Server by using Visual C# to extracting real-time hydrological data, managing data, storing the forecasted data and providing the information to the visual map-based display. The regional early flood inundation warning system use the up-to-date Web technologies driven by the database and real-time data acquisition to display the on-line forecasting flood inundation depths in the study area. The friendly interface includes on-line sequentially showing inundation area by Google Map, maximum inundation depth and its location, and providing KMZ file download of the results which can be watched on Google Earth. The developed system can provide all the relevant information and on-line forecast results that helps city authorities to make decisions during typhoon events and make actions to mitigate the losses.

Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

2015-12-01

The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.

On the reliability of seasonal climate forecasts.

PubMed

Weisheimer, A; Palmer, T N

2014-07-06

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that 'goodness' should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a '5' should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of 'goodness' rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching '5' across all regions and variables in 30 years time.

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

NASA Astrophysics Data System (ADS)

Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

2017-09-01

Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

NASA Astrophysics Data System (ADS)

Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

2016-11-01

All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

Assessing skill of a global bimonthly streamflow ensemble prediction system

NASA Astrophysics Data System (ADS)

van Dijk, A. I.; Peña-Arancibia, J.; Sheffield, J.; Wood, E. F.

2011-12-01

Ideally, a seasonal streamflow forecasting system might be conceived of as a system that ingests skillful climate forecasts from general circulation models and propagates these through thoroughly calibrated hydrological models that are initialised using hydrometric observations. In practice, there are practical problems with each of these aspects. Instead, we analysed whether a comparatively simple hydrological model-based Ensemble Prediction System (EPS) can provide global bimonthly streamflow forecasts with some skill and if so, under what circumstances the greatest skill may be expected. The system tested produces ensemble forecasts for each of six annual bimonthly periods based on the previous 30 years of global daily gridded 1° resolution climate variables and an initialised global hydrological model. To incorporate some of the skill derived from ocean conditions, a post-EPS analog method was used to sample from the ensemble based on El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) index values observed prior to the forecast. Forecasts skill was assessed through a hind-casting experiment for the period 1979-2008. Potential skill was calculated with reference to a model run with the actual forcing for the forecast period (the 'perfect' model) and was compared to actual forecast skill calculated for each of the six forecast times for an average 411 Australian and 51 pan-tropical catchments. Significant potential skill in bimonthly forecasts was largely limited to northern regions during the snow melt period, seasonally wet tropical regions at the transition of wet to dry season, and the Indonesian region where rainfall is well correlated to ENSO. The actual skill was approximately 34-50% of the potential skill. We attribute this primarily to limitations in the model structure, parameterisation and global forcing data. Use of better climate forecasts and remote sensing observations of initial catchment conditions should help to increase actual skill in future. Future work also could address the potential skill gain from using weather and climate forecasts and from a calibrated and/or alternative hydrological model or model ensemble. The approach and data might be useful as a benchmark for joint seasonal forecasting experiments planned under GEWEX.

Verifying and Postprocesing the Ensemble Spread-Error Relationship

NASA Astrophysics Data System (ADS)

Hopson, Tom; Knievel, Jason; Liu, Yubao; Roux, Gregory; Wu, Wanli

2013-04-01

With the increased utilization of ensemble forecasts in weather and hydrologic applications, there is a need to verify their benefit over less expensive deterministic forecasts. One such potential benefit of ensemble systems is their capacity to forecast their own forecast error through the ensemble spread-error relationship. The paper begins by revisiting the limitations of the Pearson correlation alone in assessing this relationship. Next, we introduce two new metrics to consider in assessing the utility an ensemble's varying dispersion. We argue there are two aspects of an ensemble's dispersion that should be assessed. First, and perhaps more fundamentally: is there enough variability in the ensembles dispersion to justify the maintenance of an expensive ensemble prediction system (EPS), irrespective of whether the EPS is well-calibrated or not? To diagnose this, the factor that controls the theoretical upper limit of the spread-error correlation can be useful. Secondly, does the variable dispersion of an ensemble relate to variable expectation of forecast error? Representing the spread-error correlation in relation to its theoretical limit can provide a simple diagnostic of this attribute. A context for these concepts is provided by assessing two operational ensembles: 30-member Western US temperature forecasts for the U.S. Army Test and Evaluation Command and 51-member Brahmaputra River flow forecasts of the Climate Forecast and Applications Project for Bangladesh. Both of these systems utilize a postprocessing technique based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. In addition, the methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. We will describe both ensemble systems briefly, review the steps used to calibrate the ensemble forecast, and present verification statistics using error-spread metrics, along with figures from operational ensemble forecasts before and after calibration.

Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

NASA Astrophysics Data System (ADS)

Zhuang, Zhaorong; Yussouf, Nusrat; Gao, Jidong

2016-05-01

As part of NOAA's "Warn-On-Forecast" initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

WOD - Weather On Demand forecasting system

NASA Astrophysics Data System (ADS)

Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

2017-04-01

The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.

Comparison of Observation Impacts in Two Forecast Systems using Adjoint Methods

NASA Technical Reports Server (NTRS)

Gelaro, Ronald; Langland, Rolf; Todling, Ricardo

2009-01-01

An experiment is being conducted to compare directly the impact of all assimilated observations on short-range forecast errors in different operational forecast systems. We use the adjoint-based method developed by Langland and Baker (2004), which allows these impacts to be efficiently calculated. This presentation describes preliminary results for a "baseline" set of observations, including both satellite radiances and conventional observations, used by the Navy/NOGAPS and NASA/GEOS-5 forecast systems for the month of January 2007. In each system, about 65% of the total reduction in 24-h forecast error is provided by satellite observations, although the impact of rawinsonde, aircraft, land, and ship-based observations remains significant. Only a small majority (50- 55%) of all observations assimilated improves the forecast, while the rest degrade it. It is found that most of the total forecast error reduction comes from observations with moderate-size innovations providing small to moderate impacts, not from outliers with very large positive or negative innovations. In a global context, the relative impacts of the major observation types are fairly similar in each system, although regional differences in observation impact can be significant. Of particular interest is the fact that while satellite radiances have a large positive impact overall, they degrade the forecast in certain locations common to both systems, especially over land and ice surfaces. Ongoing comparisons of this type, with results expected from other operational centers, should lead to more robust conclusions about the impacts of the various components of the observing system as well as about the strengths and weaknesses of the methodologies used to assimilate them.

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

NASA Astrophysics Data System (ADS)

Pappenberger, F.; Ramos, M. H.; Cloke, H. L.; Wetterhall, F.; Alfieri, L.; Bogner, K.; Mueller, A.; Salamon, P.

2015-03-01

The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are 'toughest to beat' and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon. Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naïve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better.

Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

NASA Astrophysics Data System (ADS)

Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel

2015-04-01

The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful between the 5th and the 8th day of the prediction. The information obtained is then included in an early warning system, designed in the framework of the European project iCoast (ECHO/SUB/2013/661009) with the aim of set alarms in coastal areas depending on the wave conditions, the sea level, the flooding and the run up in the coast.

Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.; Ranjithan, R. S.; Brill, E. D.

2014-08-01

Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study proposes a framework for regional water management by proposing an interbasin transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end-of-season target storage across the participating pools. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle Area. Results show that interbasin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no-transfer scenario as well as under transfers obtained with climatology; (b) spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting interbasin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating pools in the regional water supply system.

Improved Regional Water Management Utilizing Climate Forecasts: An Inter-basin Transfer Model with a Risk Management Framework

NASA Astrophysics Data System (ADS)

Li, W.; Arumugam, S.; Ranjithan, R. S.; Brill, E. D., Jr.

2014-12-01

Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study presents a framework for regional water management by proposing an Inter-Basin Transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end- of-season target storage across the participating reservoirs. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle area. Results show that inter-basin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) Inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no- transfer scenario as well as under transfers obtained with climatology; (b) Spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting inter-basin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating reservoirs in the regional water supply system.

Validation of the CME Geomagnetic Forecast Alerts Under the COMESEP Alert System

NASA Astrophysics Data System (ADS)

Dumbović, Mateja; Srivastava, Nandita; Rao, Yamini K.; Vršnak, Bojan; Devos, Andy; Rodriguez, Luciano

2017-08-01

Under the European Union 7th Framework Programme (EU FP7) project Coronal Mass Ejections and Solar Energetic Particles (COMESEP, http://comesep.aeronomy.be), an automated space weather alert system has been developed to forecast solar energetic particles (SEP) and coronal mass ejection (CME) risk levels at Earth. The COMESEP alert system uses the automated detection tool called Computer Aided CME Tracking (CACTus) to detect potentially threatening CMEs, a drag-based model (DBM) to predict their arrival, and a CME geoeffectiveness tool (CGFT) to predict their geomagnetic impact. Whenever CACTus detects a halo or partial halo CME and issues an alert, the DBM calculates its arrival time at Earth and the CGFT calculates its geomagnetic risk level. The geomagnetic risk level is calculated based on an estimation of the CME arrival probability and its likely geoeffectiveness, as well as an estimate of the geomagnetic storm duration. We present the evaluation of the CME risk level forecast with the COMESEP alert system based on a study of geoeffective CMEs observed during 2014. The validation of the forecast tool is made by comparing the forecasts with observations. In addition, we test the success rate of the automatic forecasts (without human intervention) against the forecasts with human intervention using advanced versions of the DBM and CGFT (independent tools available at the Hvar Observatory website, http://oh.geof.unizg.hr). The results indicate that the success rate of the forecast in its current form is unacceptably low for a realistic operation system. Human intervention improves the forecast, but the false-alarm rate remains unacceptably high. We discuss these results and their implications for possible improvement of the COMESEP alert system.

An Integrated Urban Flood Analysis System in South Korea

NASA Astrophysics Data System (ADS)

Moon, Young-Il; Kim, Min-Seok; Yoon, Tae-Hyung; Choi, Ji-Hyeok

2017-04-01

Due to climate change and the rapid growth of urbanization, the frequency of concentrated heavy rainfall has caused urban floods. As a result, we studied climate change in Korea and developed an integrated flood analysis system that systematized technology to quantify flood risk and flood forecasting in urban areas. This system supports synthetic decision-making through real-time monitoring and prediction on flash rain or short-term rainfall by using radar and satellite information. As part of the measures to deal with the increase of inland flood damage, we have found it necessary to build a systematic city flood prevention system that systematizes technology to quantify flood risk as well as flood forecast, taking into consideration both inland and river water. This combined inland-river flood analysis system conducts prediction on flash rain or short-term rainfall by using radar and satellite information and performs prompt and accurate prediction on the inland flooded area. In addition, flood forecasts should be accurate and immediate. Accurate flood forecasts signify that the prediction of the watch, warning time and water level is precise. Immediate flood forecasts represent the forecasts lead time which is the time needed to evacuate. Therefore, in this study, in order to apply rainfall-runoff method to medium and small urban stream for flood forecasts, short-term rainfall forecasting using radar is applied to improve immediacy. Finally, it supports synthetic decision-making for prevention of flood disaster through real-time monitoring. Keywords: Urban Flood, Integrated flood analysis system, Rainfall forecasting, Korea Acknowledgments This research was supported by a grant (16AWMP-B066744-04) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

Forecasting the short-term passenger flow on high-speed railway with neural networks.

PubMed

Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing

2014-01-01

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.

NCAR's Experimental Real-time Convection-allowing Ensemble Prediction System

NASA Astrophysics Data System (ADS)

Schwartz, C. S.; Romine, G. S.; Sobash, R.; Fossell, K.

2016-12-01

Since April 2015, the National Center for Atmospheric Research's (NCAR's) Mesoscale and Microscale Meteorology (MMM) Laboratory, in collaboration with NCAR's Computational Information Systems Laboratory (CISL), has been producing daily, real-time, 10-member, 48-hr ensemble forecasts with 3-km horizontal grid spacing over the conterminous United States (http://ensemble.ucar.edu). These computationally-intensive, next-generation forecasts are produced on the Yellowstone supercomputer, have been embraced by both amateur and professional weather forecasters, are widely used by NCAR and university researchers, and receive considerable attention on social media. Initial conditions are supplied by NCAR's Data Assimilation Research Testbed (DART) software and the forecast model is NCAR's Weather Research and Forecasting (WRF) model; both WRF and DART are community tools. This presentation will focus on cutting-edge research results leveraging the ensemble dataset, including winter weather predictability, severe weather forecasting, and power outage modeling. Additionally, the unique design of the real-time analysis and forecast system and computational challenges and solutions will be described.

Forecasting the spatial transmission of influenza in the United States.

PubMed

Pei, Sen; Kandula, Sasikiran; Yang, Wan; Shaman, Jeffrey

2018-03-13

Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,-i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold-up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.

Statistical Analysis of Atmospheric Forecast Model Accuracy - A Focus on Multiple Atmospheric Variables and Location-Based Analysis

DTIC Science & Technology

2014-04-01

WRF ) model is a numerical weather prediction system designed for operational forecasting and atmospheric research. This report examined WRF model... WRF , weather research and forecasting, atmospheric effects 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18. NUMBER OF...and Forecasting ( WRF ) model. The authors would also like to thank Ms. Sherry Larson, STS Systems Integration, LLC, ARL Technical Publishing Branch

An experimental system for flood risk forecasting and monitoring at global scale

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Alfieri, Lorenzo; Kalas, Milan; Lorini, Valerio; Salamon, Peter

2017-04-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by a wide range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasting, combining streamflow estimations with expected inundated areas and flood impacts. Finally, emerging technologies such as crowdsourcing and social media monitoring can play a crucial role in flood disaster management and preparedness. Here, we present some recent advances of an experimental procedure for near-real time flood mapping and impact assessment. The procedure translates in near real-time the daily streamflow forecasts issued by the Global Flood Awareness System (GloFAS) into event-based flood hazard maps, which are then combined with exposure and vulnerability information at global scale to derive risk forecast. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To increase the reliability of our forecasts we propose the integration of model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification and correction of impact forecasts. Finally, we present the results of preliminary tests which show the potential of the proposed procedure in supporting emergency response and management.

Skilful seasonal forecasts of streamflow over Europe?

NASA Astrophysics Data System (ADS)

Arnal, Louise; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

2018-04-01

This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season.

PubMed

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-09-01

Environment and Climate Change Canada's FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2-July 15, and August 15-31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of -7.3 µg m(-3) and 3.1 µg m(-3)), it showed better forecast skill than the RAQDPS (MB of -11.7 µg m(-3) and -5.8 µg m(-3)) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m(-3) also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.

An investigation into incident duration forecasting for FleetForward

DOT National Transportation Integrated Search

2000-08-01

Traffic condition forecasting is the process of estimating future traffic conditions based on current and archived data. Real-time forecasting is becoming an important tool in Intelligent Transportation Systems (ITS). This type of forecasting allows ...

Satellite provided fixed communications services: A forecast of potential domestic demand through the year 2000: Volume 2: Main text

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Potential satellite-provided fixed communications services, baseline forecasts, net long haul forecasts, cost analysis, net addressable forecasts, capacity requirements, and satellite system market development are considered.

Scientific assessment of accuracy, skill and reliability of ocean probabilistic forecast products.

NASA Astrophysics Data System (ADS)

Wei, M.; Rowley, C. D.; Barron, C. N.; Hogan, P. J.

2016-02-01

As ocean operational centers are increasingly adopting and generating probabilistic forecast products for their customers with valuable forecast uncertainties, how to assess and measure these complicated probabilistic forecast products objectively is challenging. The first challenge is how to deal with the huge amount of the data from the ensemble forecasts. The second one is how to describe the scientific quality of probabilistic products. In fact, probabilistic forecast accuracy, skills, reliability, resolutions are different attributes of a forecast system. We briefly introduce some of the fundamental metrics such as the Reliability Diagram, Reliability, Resolution, Brier Score (BS), Brier Skill Score (BSS), Ranked Probability Score (RPS), Ranked Probability Skill Score (RPSS), Continuous Ranked Probability Score (CRPS), and Continuous Ranked Probability Skill Score (CRPSS). The values and significance of these metrics are demonstrated for the forecasts from the US Navy's regional ensemble system with different ensemble members. The advantages and differences of these metrics are studied and clarified.

Integrating Measurement Based New Knowledge on Wildland Fire Emissions and Chemistry into the AIRPACT Air Quality Forecasting for the Pacific Northwest

NASA Astrophysics Data System (ADS)

Nergui, T.; Lee, Y.; Chung, S. H.; Lamb, B. K.; Yokelson, R. J.; Barsanti, K.

2017-12-01

A number of chamber and field measurements have shown that atmospheric organic aerosols and their precursors produced from wildfires are significantly underestimated in the emission inventories used for air quality models for various applications such as regulatory strategy development, impact assessments of air pollutants, and air quality forecasting for public health. The AIRPACT real-time air quality forecasting system consistently underestimates surface level fine particulate matter (PM2.5) concentrations in the summer at both urban and rural locations in the Pacific Northwest, primarily result of errors in organic particulate matter. In this work, we implement updated chemical speciation and emission factors based on FLAME-IV (Fourth Fire Lab at Missoula Experiment) and other measurements in the Blue-Sky fire emission model and the SMOKE emission preprocessor; and modified parameters for the secondary organic aerosol (SOA) module in CMAQ chemical transport model of the AIRPACT modeling system. Simulation results from CMAQ version 5.2 which has a better treatment for anthropogenic SOA formation (as a base case) and modified parameterization used for fire emissions and chemistry in the model (fire-soa case) are evaluated against airborne measurements downwind of the Big Windy Complex Fire and the Colockum Tarps Fire, both of which occurred in the Pacific Northwest in summer 2013. Using the observed aerosol chemical composition and mass loadings for organics, nitrate, sulfate, ammonium, and chloride from aircraft measurements during the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS) and the Biomass Burning Observation Project (BBOP), we assess how new knowledge gained from wildfire measurements improve model predictions for SOA and its contribution to the total mass of PM2.5 concentrations.

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, J.; Hodge, B. M.; Florita, A.

2013-10-01

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The resultsmore » show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.« less

Automated Dynamic Demand Response Implementation on a Micro-grid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuppannagari, Sanmukh R.; Kannan, Rajgopal; Chelmis, Charalampos

In this paper, we describe a system for real-time automated Dynamic and Sustainable Demand Response with sparse data consumption prediction implemented on the University of Southern California campus microgrid. Supply side approaches to resolving energy supply-load imbalance do not work at high levels of renewable energy penetration. Dynamic Demand Response (D 2R) is a widely used demand-side technique to dynamically adjust electricity consumption during peak load periods. Our D 2R system consists of accurate machine learning based energy consumption forecasting models that work with sparse data coupled with fast and sustainable load curtailment optimization algorithms that provide the ability tomore » dynamically adapt to changing supply-load imbalances in near real-time. Our Sustainable DR (SDR) algorithms attempt to distribute customer curtailment evenly across sub-intervals during a DR event and avoid expensive demand peaks during a few sub-intervals. It also ensures that each customer is penalized fairly in order to achieve the targeted curtailment. We develop near linear-time constant-factor approximation algorithms along with Polynomial Time Approximation Schemes (PTAS) for SDR curtailment that minimizes the curtailment error defined as the difference between the target and achieved curtailment values. Our SDR curtailment problem is formulated as an Integer Linear Program that optimally matches customers to curtailment strategies during a DR event while also explicitly accounting for customer strategy switching overhead as a constraint. We demonstrate the results of our D 2R system using real data from experiments performed on the USC smartgrid and show that 1) our prediction algorithms can very accurately predict energy consumption even with noisy or missing data and 2) our curtailment algorithms deliver DR with extremely low curtailment errors in the 0.01-0.05 kWh range.« less

The development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish rivers

NASA Astrophysics Data System (ADS)

Foster, Kean; Bertacchi Uvo, Cintia; Olsson, Jonas

2018-05-01

Hydropower makes up nearly half of Sweden's electrical energy production. However, the distribution of the water resources is not aligned with demand, as most of the inflows to the reservoirs occur during the spring flood period. This means that carefully planned reservoir management is required to help redistribute water resources to ensure optimal production and accurate forecasts of the spring flood volume (SFV) is essential for this. The current operational SFV forecasts use a historical ensemble approach where the HBV model is forced with historical observations of precipitation and temperature. In this work we develop and test a multi-model prototype, building on previous work, and evaluate its ability to forecast the SFV in 84 sub-basins in northern Sweden. The hypothesis explored in this work is that a multi-model seasonal forecast system incorporating different modelling approaches is generally more skilful at forecasting the SFV in snow dominated regions than a forecast system that utilises only one approach. The testing is done using cross-validated hindcasts for the period 1981-2015 and the results are evaluated against both climatology and the current system to determine skill. Both the multi-model methods considered showed skill over the reference forecasts. The version that combined the historical modelling chain, dynamical modelling chain, and statistical modelling chain performed better than the other and was chosen for the prototype. The prototype was able to outperform the current operational system 57 % of the time on average and reduce the error in the SFV by ˜ 6 % across all sub-basins and forecast dates.

Seasonal forecasting of groundwater levels in natural aquifers in the United Kingdom

NASA Astrophysics Data System (ADS)

Mackay, Jonathan; Jackson, Christopher; Pachocka, Magdalena; Brookshaw, Anca; Scaife, Adam

2014-05-01

Groundwater aquifers comprise the world's largest freshwater resource and provide resilience to climate extremes which could become more frequent under future climate changes. Prolonged dry conditions can induce groundwater drought, often characterised by significantly low groundwater levels which may persist for months to years. In contrast, lasting wet conditions can result in anomalously high groundwater levels which result in flooding, potentially at large economic cost. Using computational models to produce groundwater level forecasts allows appropriate management strategies to be considered in advance of extreme events. The majority of groundwater level forecasting studies to date use data-based models, which exploit the long response time of groundwater levels to meteorological drivers and make forecasts based only on the current state of the system. Instead, seasonal meteorological forecasts can be used to drive hydrological models and simulate groundwater levels months into the future. Such approaches have not been used in the past due to a lack of skill in these long-range forecast products. However systems such as the latest version of the Met Office Global Seasonal Forecast System (GloSea5) are now showing increased skill up to a 3-month lead time. We demonstrate the first groundwater level ensemble forecasting system using a multi-member ensemble of hindcasts from GloSea5 between 1996 and 2009 to force 21 simple lumped conceptual groundwater models covering most of the UK's major aquifers. We present the results from this hindcasting study and demonstrate that the system can be used to forecast groundwater levels with some skill up to three months into the future.

Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

NASA Astrophysics Data System (ADS)

van der Zwan, Rene

2013-04-01

The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water management, including temporary lower storage basin levels and a reduction in extra investments for infrastructural measures.

Short-term acoustic forecasting via artificial neural networks for neonatal intensive care units.

PubMed

Young, Jason; Macke, Christopher J; Tsoukalas, Lefteri H

2012-11-01

Noise levels in hospitals, especially neonatal intensive care units (NICUs), have become of great concern for hospital designers. This paper details an artificial neural network (ANN) approach to forecasting the sound loads in NICUs. The ANN is used to learn the relationship between past, present, and future noise levels. By training the ANN with data specific to the location and device used to measure the sound, the ANN is able to produce reasonable predictions of noise levels in the NICU. Best case results show average absolute errors of 5.06 ± 4.04% when used to predict the noise levels one hour ahead, which correspond to 2.53 dBA ± 2.02 dBA. The ANN has the tendency to overpredict during periods of stability and underpredict during large transients. This forecasting algorithm could be of use in any application where prediction and prevention of harmful noise levels are of the utmost concern.

Assessment of an ensemble seasonal streamflow forecasting system for Australia

NASA Astrophysics Data System (ADS)

Bennett, James C.; Wang, Quan J.; Robertson, David E.; Schepen, Andrew; Li, Ming; Michael, Kelvin

2017-11-01

Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall-runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall-runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.

ENSO Prediction in the NASA GMAO GEOS-5 Seasonal Forecasting System

NASA Astrophysics Data System (ADS)

Kovach, R. M.; Borovikov, A.; Marshak, J.; Pawson, S.; Vernieres, G.

2016-12-01

Seasonal-to-Interannual coupled forecasts are conducted in near-real time with the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model (AOGCM). A 30-year suite of 9-month hindcasts is available, initialized with the MERRA-Ocean, MERRA-Land, and MERRA atmospheric fields. These forecasts are used to predict the timing and magnitude of ENSO and other short-term climate variability. The 2015 El Niño peaked in November 2015 and was considered a "very strong" event with the Equatorial Pacific Ocean sea-surface-temperature (SST) anomalies higher than 2.0 °C. These very strong temperature anomalies began in Sep/Oct/Nov (SON) of 2015 and persisted through Dec/Jan/Feb (DJF) of 2016. The other two very strong El Niño events recently recorded occurred in 1981/82 and 1997/98. The GEOS-5 system began predicting a very strong El Niño for SON starting with the March 2015 forecast. At this time, the GMAO forecast was an outlier in both the NMME and IRI multi-model ensemble prediction plumes. The GMAO May 2015 forecast for the November 2015 peak in temperature anomaly in the Niño3.4 region was in excellent agreement with the real event, but in May this forecast was still one of the outliers in the multi-model forecasts. The GEOS-5 May 2015 forecast also correctly predicted the weakening of the Eastern Pacific (Niño1+2) anomalies for SON. We will present a summary of the NASA GMAO GEOS-5 Seasonal Forecast System skills based on historic hindcasts. Initial conditions, prediction of ocean surface and subsurface evolution for the 2015/16 El Niño will be compared to the 1998/97 event. GEOS-5 capability to predict the precipitation, i.e. to model the teleconnection patterns associated with El Niño will also be shown. To conclude, we will highlight some new developments in the GEOS forecasting system.

Against all odds -- Probabilistic forecasts and decision making

NASA Astrophysics Data System (ADS)

Liechti, Katharina; Zappa, Massimiliano

2015-04-01

In the city of Zurich (Switzerland) the setting is such that the damage potential due to flooding of the river Sihl is estimated to about 5 billion US dollars. The flood forecasting system that is used by the administration for decision making runs continuously since 2007. It has a time horizon of max. five days and operates at hourly time steps. The flood forecasting system includes three different model chains. Two of those are run by the deterministic NWP models COSMO-2 and COSMO-7 and one is driven by the probabilistic NWP COSMO-Leps. The model chains are consistent since February 2010, so five full years are available for the evaluation for the system. The system was evaluated continuously and is a very nice example to present the added value that lies in probabilistic forecasts. The forecasts are available on an online-platform to the decision makers. Several graphical representations of the forecasts and forecast-history are available to support decision making and to rate the current situation. The communication between forecasters and decision-makers is quite close. To put it short, an ideal situation. However, an event or better put a non-event in summer 2014 showed that the knowledge about the general superiority of probabilistic forecasts doesn't necessarily mean that the decisions taken in a specific situation will be based on that probabilistic forecast. Some years of experience allow gaining confidence in the system, both for the forecasters and for the decision-makers. Even if from the theoretical point of view the handling during crisis situation is well designed, a first event demonstrated that the dialog with the decision-makers still lacks of exercise during such situations. We argue, that a false alarm is a needed experience to consolidate real-time emergency procedures relying on ensemble predictions. A missed event would probably also fit, but, in our case, we are very happy not to report about this option.

Evaluation of Clear-Air Turbulence Diagnostics: GTG in Korea

NASA Astrophysics Data System (ADS)

Kim, J.-H.; Chun, H.-Y.; Jang, W.; Sharman, R. D.

2009-04-01

Turbulence forecasting algorithm, the Graphical Turbulence Guidance (GTG) system developed at NCAR (Sharman et al., 2006), is evaluated with available turbulence observations (e.g. pilot reports; PIREPs) reported in South Korea during the recent 4 years (2003-2007). Clear-air turbulence (CAT) is extracted from PIREPs by using cloud-to-ground lightning flash data from Korean Meteorological Administration (KMA). The GTG system includes several steps. First, 45 turbulence indices are calculated in the East Asian region near Korean peninsula using the Regional Data Assimilation and Prediction System (RDAPS) analysis data with 30 km horizontal grid spacing provided by KMA. Second, 10 CAT indices that performed ten best forecasting score are selected. The scoring method is based on the probability of detection, which is calculated using PIREPs exclusively of moderate-or-greater intensity. Various statistical examinations and sensitivity tests of the GTG system are performed by yearly and seasonally classified PIREPs in South Korea. Performance of GTG is more consistent and stable than that of any individual diagnostic in each year and season. In addition, current-year forecasting based on yearly PIREPs is better than adjacent-year forecasting and year-after-year forecasting. Seasonal forecasting is generally better than yearly forecasting, because selected CAT indices in each season represent meteorological condition much more properly than applying the selected CAT indices to all seasons. Wintertime forecasting is the best among the four seasonal forecastings. This is likely due to that the GTG system consists of many CAT indices related to jet stream, and turbulence associated with the jet can be most activated in wintertime under strong jet magnitude. On the other hand, summertime forecasting skill is much less than in wintertime. To acquire better performance for summertime forecasting, it is likely to develop more turbulence indices related to, for example, convections. By sensitivity test to the number of combined indices, it is found that yearly and seasonal GTG is the best when about 7 CAT indices are combined.

Some economic benefits of a synchronous earth observatory satellite

NASA Technical Reports Server (NTRS)

Battacharyya, R. K.; Greenberg, J. S.; Lowe, D. S.; Sattinger, I. J.

1974-01-01

An analysis was made of the economic benefits which might be derived from reduced forecasting errors made possible by data obtained from a synchronous satellite system which can collect earth observation and meteorological data continuously and on demand. User costs directly associated with achieving benefits are included. In the analysis, benefits were evaluated which might be obtained as a result of improved thunderstorm forecasting, frost warning, and grain harvest forecasting capabilities. The anticipated system capabilities were used to arrive at realistic estimates of system performance on which to base the benefit analysis. Emphasis was placed on the benefits which result from system forecasting accuracies. Benefits from improved thunderstorm forecasts are indicated for the construction, air transportation, and agricultural industries. The effects of improved frost warning capability on the citrus crop are determined. The benefits from improved grain forecasting capability are evaluated in terms of both U.S. benefits resulting from domestic grain distribution and U.S. benefits from international grain distribution.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

Human-model hybrid Korean air quality forecasting system.

PubMed

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.

Optimising seasonal streamflow forecast lead time for operational decision making in Australia

NASA Astrophysics Data System (ADS)

Schepen, Andrew; Zhao, Tongtiegang; Wang, Q. J.; Zhou, Senlin; Feikema, Paul

2016-10-01

Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0-14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to -15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.

Real-time Social Internet Data to Guide Forecasting Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Del Valle, Sara Y.

Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematicalmore » approaches and heterogeneous data streams.« less

Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

NASA Astrophysics Data System (ADS)

Jha, Sanjeev K.; Shrestha, Durga L.; Stadnyk, Tricia A.; Coulibaly, Paulin

2018-03-01

Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

Flood Forecast Accuracy and Decision Support System Approach: the Venice Case

NASA Astrophysics Data System (ADS)

Canestrelli, A.; Di Donato, M.

2016-02-01

In the recent years numerical models for weather predictions have experienced continuous advances in technology. As a result, all the disciplines making use of weather forecasts have made significant steps forward. In the case of the Safeguard of Venice, a large effort has been put in order to improve the forecast of tidal levels. In this context, the Istituzione Centro Previsioni e Segnalazioni Maree (ICPSM) of the Venice Municipality has developed and tested many different forecast models, both of the statistical and deterministic type, and has shown to produce very accurate forecasts. For Venice, the maximum admissible forecast error should be (ideally) of the order of ten centimeters at 24 hours. The entity of the forecast error clearly affects the decisional process, which mainly consists of alerting the population, activating the movable barriers installed at the three tidal inlets and contacting the port authority. This process becomes more challenging whenever the weather predictions, and therefore the water level forecasts, suddenly change. These new forecasts have to be quickly transformed into operational tasks. Therefore, it is of the utter importance to set up scheduled alerts and emergency plans by means of easy-to-follow procedures. On this direction, Technital has set up a Decision Support System based on expert procedures that minimizes the human mistakes and, as a consequence, reduces the risk of flooding of the historical center. Moreover, the Decision Support System can communicate predefined alerts to all the interested subjects. The System uses the water levels forecasts produced by the ICPSM by taking into account the accuracy at different leading times. The Decision Support System has been successfully tested with 8 years of data, 6 of them in real time. Venice experience shows that the Decision Support System is an essential tool which assesses the risks associated with a particular event, provides clear operational procedures and minimizes the impact of natural floods on human lives, private properties and historical monuments.

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R. W.

2008-12-01

The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Can regional climate models provide additional useful information from global seasonal forecasts? MRED will use a suite of regional climate models to downscale seasonal forecasts produced by the new National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus will be on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the potential usefulness of higher resolution, especially for near-surface fields influenced by high resolution orography. Each regional model will cover the conterminous US (CONUS) at approximately 32 km resolution, and will perform an ensemble of 15 runs for each year 1982-2003 for the forecast period 1 December - 30 April. MRED will compare individual regional and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs), as well as wind, humidity, radiation, turbulent heat fluxes, which are important for more advanced coupled macro-scale hydrologic models. Metrics of ensemble spread will also be evaluated. Extensive analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will eventually define a strategy for more skillful and useful regional seasonal climate forecasts.

Regional Model Nesting Within GFS Daily Forecasts Over West Africa

NASA Technical Reports Server (NTRS)

Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

2010-01-01

The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger is shown.

Organic-matter loading determines regime shifts and alternative states in an aquatic ecosystem

PubMed Central

Sirota, Jennie; Baiser, Benjamin; Gotelli, Nicholas J.; Ellison, Aaron M.

2013-01-01

Slow changes in underlying state variables can lead to “tipping points,” rapid transitions between alternative states (“regime shifts”) in a wide range of complex systems. Tipping points and regime shifts routinely are documented retrospectively in long time series of observational data. Experimental induction of tipping points and regime shifts is rare, but could lead to new methods for detecting impending tipping points and forestalling regime shifts. By using controlled additions of detrital organic matter (dried, ground arthropod prey), we experimentally induced a shift from aerobic to anaerobic states in a miniature aquatic ecosystem: the self-contained pools that form in leaves of the carnivorous northern pitcher plant, Sarracenia purpurea. In unfed controls, the concentration of dissolved oxygen ([O2]) in all replicates exhibited regular diurnal cycles associated with daytime photosynthesis and nocturnal plant respiration. In low prey-addition treatments, the regular diurnal cycles of [O2] were disrupted, but a regime shift was not detected. In high prey-addition treatments, the variance of the [O2] time series increased until the system tipped from an aerobic to an anaerobic state. In these treatments, replicate [O2] time series predictably crossed a tipping point at ∼45 h as [O2] was decoupled from diurnal cycles of photosynthesis and respiration. Increasing organic-matter loading led to predictable changes in [O2] dynamics, with high loading consistently driving the system past a well-defined tipping point. The Sarracenia microecosystem functions as a tractable experimental system in which to explore the forecasting and management of tipping points and alternative regimes. PMID:23613583

Organic-matter loading determines regime shifts and alternative states in an aquatic ecosystem.

PubMed

Sirota, Jennie; Baiser, Benjamin; Gotelli, Nicholas J; Ellison, Aaron M

2013-05-07

Slow changes in underlying state variables can lead to "tipping points," rapid transitions between alternative states ("regime shifts") in a wide range of complex systems. Tipping points and regime shifts routinely are documented retrospectively in long time series of observational data. Experimental induction of tipping points and regime shifts is rare, but could lead to new methods for detecting impending tipping points and forestalling regime shifts. By using controlled additions of detrital organic matter (dried, ground arthropod prey), we experimentally induced a shift from aerobic to anaerobic states in a miniature aquatic ecosystem: the self-contained pools that form in leaves of the carnivorous northern pitcher plant, Sarracenia purpurea. In unfed controls, the concentration of dissolved oxygen ([O2]) in all replicates exhibited regular diurnal cycles associated with daytime photosynthesis and nocturnal plant respiration. In low prey-addition treatments, the regular diurnal cycles of [O2] were disrupted, but a regime shift was not detected. In high prey-addition treatments, the variance of the [O2] time series increased until the system tipped from an aerobic to an anaerobic state. In these treatments, replicate [O2] time series predictably crossed a tipping point at ~45 h as [O2] was decoupled from diurnal cycles of photosynthesis and respiration. Increasing organic-matter loading led to predictable changes in [O2] dynamics, with high loading consistently driving the system past a well-defined tipping point. The Sarracenia microecosystem functions as a tractable experimental system in which to explore the forecasting and management of tipping points and alternative regimes.

MAFALDA: An early warning modeling tool to forecast volcanic ash dispersal and deposition

NASA Astrophysics Data System (ADS)

Barsotti, S.; Nannipieri, L.; Neri, A.

2008-12-01

Forecasting the dispersal of ash from explosive volcanoes is a scientific challenge to modern volcanology. It also represents a fundamental step in mitigating the potential impact of volcanic ash on urban areas and transport routes near explosive volcanoes. To this end we developed a Web-based early warning modeling tool named MAFALDA (Modeling and Forecasting Ash Loading and Dispersal in the Atmosphere) able to quantitatively forecast ash concentrations in the air and on the ground. The main features of MAFALDA are the usage of (1) a dispersal model, named VOL-CALPUFF, that couples the column ascent phase with the ash cloud transport and (2) high-resolution weather forecasting data, the capability to run and merge multiple scenarios, and the Web-based structure of the procedure that makes it suitable as an early warning tool. MAFALDA produces plots for a detailed analysis of ash cloud dynamics and ground deposition, as well as synthetic 2-D maps of areas potentially affected by dangerous concentrations of ash. A first application of MAFALDA to the long-lasting weak plumes produced at Mt. Etna (Italy) is presented. A similar tool can be useful to civil protection authorities and volcanic observatories in reducing the impact of the eruptive events. MAFALDA can be accessed at http://mafalda.pi.ingv.it.

Long-term volcanic hazard forecasts based on Somma-Vesuvio past eruptive activity

NASA Astrophysics Data System (ADS)

Lirer, Lucio; Petrosino, Paola; Alberico, Ines; Postiglione, Immacolata

2001-02-01

Distributions of pyroclastic deposits from the main explosive events at Somma-Vesuvio during the 8,000-year B.P.-A.D. 1906 time-span have been analysed to provide maps of volcanic hazard for long-term eruption forecasting. In order to define hazard ratings, the spatial distributions and loads (kg/m2) exerted by the fall deposits on the roofs of buildings have been considered. A load higher than 300 kg/m2 is defined as destructive. The relationship load/frequency (the latter defined as the number of times that an area has been impacted by the deposition of fall deposits) is considered to be a suitable parameter for differentiating among areas according to hazard rating. Using past fall deposit distributions as the basis for future eruptive scenarios, the total area that could be affected by the products of a future Vesuvio explosive eruption is 1,500 km2. The perivolcanic area (274 km2) has the greatest hazard rating because it could be buried by pyroclastic flow deposits thicker than 0.5 m and up to several tens of metres in thickness. Currently, the perivolcanic area also has the highest risk because of the high exposed value, mainly arising from the high population density.

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

2014-01-01

 The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)

NASA Astrophysics Data System (ADS)

Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.

2013-12-01

The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in near-time hydrologic forecast applications (Hirpa et al. 2013). More recent efforts during this year's monsoon season are optimally combining these different independent sources of river forecast information along with archived flood inundation imagery of the Dartmouth Flood Observatory to improve the visualization and overall skill of the ongoing CFAB ensemble weather forecast-based flood forecasting system within the unique context of the ongoing flood forecasting efforts for Bangladesh.

Enhanced seasonal forecast skill following stratospheric sudden warmings

NASA Astrophysics Data System (ADS)

Sigmond, M.; Scinocca, J. F.; Kharin, V. V.; Shepherd, T. G.

2013-02-01

Advances in seasonal forecasting have brought widespread socio-economic benefits. However, seasonal forecast skill in the extratropics is relatively modest, prompting the seasonal forecasting community to search for additional sources of predictability. For over a decade it has been suggested that knowledge of the state of the stratosphere can act as a source of enhanced seasonal predictability; long-lived circulation anomalies in the lower stratosphere that follow stratospheric sudden warmings are associated with circulation anomalies in the troposphere that can last up to two months. Here, we show by performing retrospective ensemble model forecasts that such enhanced predictability can be realized in a dynamical seasonal forecast system with a good representation of the stratosphere. When initialized at the onset date of stratospheric sudden warmings, the model forecasts faithfully reproduce the observed mean tropospheric conditions in the months following the stratospheric sudden warmings. Compared with an equivalent set of forecasts that are not initialized during stratospheric sudden warmings, we document enhanced forecast skill for atmospheric circulation patterns, surface temperatures over northern Russia and eastern Canada and North Atlantic precipitation. We suggest that seasonal forecast systems initialized during stratospheric sudden warmings are likely to yield significantly greater forecast skill in some regions.

Integrated Forecast-Decision Systems For River Basin Planning and Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.

2005-12-01

A central application of climatology, meteorology, and hydrology is the generation of reliable forecasts for water resources management. In principle, effective use of forecasts could improve water resources management by providing extra protection against floods, mitigating the adverse effects of droughts, generating more hydropower, facilitating recreational activities, and minimizing the impacts of extreme events on the environment and the ecosystems. In practice, however, realization of these benefits depends on three requisite elements. First is the skill and reliability of forecasts. Second is the existence of decision support methods/systems with the ability to properly utilize forecast information. And third is the capacity of the institutional infrastructure to incorporate the information provided by the decision support systems into the decision making processes. This presentation discusses several decision support systems (DSS) using ensemble forecasting that have been developed by the Georgia Water Resources Institute for river basin management. These DSS are currently operational in Africa, Europe, and the US and address integrated water resources and energy planning and management in river basins with multiple water uses, multiple relevant temporal and spatial scales, and multiple decision makers. The article discusses the methods used and advocates that the design, development, and implementation of effective forecast-decision support systems must bring together disciplines, people, and institutions necessary to address today's complex water resources challenges.

A PERFORMANCE EVALUATION OF THE ETA- CMAQ AIR QUALITY FORECAST SYSTEM FOR THE SUMMER OF 2005

EPA Science Inventory

This poster presents an evaluation of the Eta-CMAQ Air Quality Forecast System's experimental domain using O₃ observations obtained from EPA's AIRNOW program and a suite of statistical metrics examining both discrete and categorical forecasts.

A Decision Support System for effective use of probability forecasts

NASA Astrophysics Data System (ADS)

De Kleermaeker, Simone; Verkade, Jan

2013-04-01

Often, water management decisions are based on hydrological forecasts. These forecasts, however, are affected by inherent uncertainties. It is increasingly common for forecasting agencies to make explicit estimates of these uncertainties and thus produce probabilistic forecasts. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a stricter separation of responsibilities between forecasters and decision maker can be made. However, simply having probabilistic forecasts available is not sufficient to realise the associated benefits. Additional effort is required in areas such as forecast visualisation and communication, decision making in uncertainty and forecast verification. Also, revised separation of responsibilities requires a shift in institutional arrangements and responsibilities. A recent study identified a number of additional issues related to the effective use of probability forecasts. When moving from deterministic to probability forecasting, a dimension is added to an already multi-dimensional problem; this makes it increasingly difficult for forecast users to extract relevant information from a forecast. A second issue is that while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be present. For example, in many cases no estimates of flood damage, of costs of management measures and of damage reduction are available. This paper presents the results of the study, including some suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development of the DSS is outlined.

Uncertainty quantification and reliability assessment in operational oil spill forecast modeling system.

PubMed

Hou, Xianlong; Hodges, Ben R; Feng, Dongyu; Liu, Qixiao

2017-03-15

As oil transport increasing in the Texas bays, greater risks of ship collisions will become a challenge, yielding oil spill accidents as a consequence. To minimize the ecological damage and optimize rapid response, emergency managers need to be informed with how fast and where oil will spread as soon as possible after a spill. The state-of-the-art operational oil spill forecast modeling system improves the oil spill response into a new stage. However uncertainty due to predicted data inputs often elicits compromise on the reliability of the forecast result, leading to misdirection in contingency planning. Thus understanding the forecast uncertainty and reliability become significant. In this paper, Monte Carlo simulation is implemented to provide parameters to generate forecast probability maps. The oil spill forecast uncertainty is thus quantified by comparing the forecast probability map and the associated hindcast simulation. A HyosPy-based simple statistic model is developed to assess the reliability of an oil spill forecast in term of belief degree. The technologies developed in this study create a prototype for uncertainty and reliability analysis in numerical oil spill forecast modeling system, providing emergency managers to improve the capability of real time operational oil spill response and impact assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

DOE PAGES

Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; ...

2015-11-10

Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

Nationwide validation of ensemble streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Lee, H. S.; Liu, Y.; Ward, J.; Brown, J.; Maestre, A.; Herr, H.; Fresch, M. A.; Wells, E.; Reed, S. M.; Jones, E.

2017-12-01

The National Weather Service's (NWS) Office of Water Prediction (OWP) recently launched a nationwide effort to verify streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for a majority of forecast locations across the 13 River Forecast Centers (RFCs). Known as the HEFS Baseline Validation (BV), the project involves a joint effort between the OWP and the RFCs. It aims to provide a geographically consistent, statistically robust validation, and a benchmark to guide the operational implementation of the HEFS, inform practical applications, such as impact-based decision support services, and to provide an objective framework for evaluating strategic investments in the HEFS. For the BV, HEFS hindcasts are issued once per day on a 12Z cycle for the period of 1985-2015 with a forecast horizon of 30 days. For the first two weeks, the hindcasts are forced with precipitation and temperature ensemble forecasts from the Global Ensemble Forecast System of the National Centers for Environmental Prediction, and by resampled climatology for the remaining period. The HEFS-generated ensemble streamflow hindcasts are verified using the Ensemble Verification System. Skill is assessed relative to streamflow hindcasts generated from NWS' current operational system, namely climatology-based Ensemble Streamflow Prediction. In this presentation, we summarize the results and findings to date.

Theoretical Models for Aircraft Availability: Classical Approach to Identification of Trends, Seasonality, and System Constraints in the Development of Realized Models

DTIC Science & Technology

2004-03-01

predicting future events ( Heizer and Render , 1999). Forecasting techniques fall into two major categories, qualitative and quantitative methods...Globemaster III.” Excerpt from website. www.globalsecurity.org/military /systems/ aircraft/c-17-history.htm. 2003. Heizer , Jay, and Barry Render ...of the past data used to make the forecast ( Heizer , et. al., 1999). Explanatory forecasting models assume that the variable being forecasted

A report from the Space Science and Engineering Center, the University of Wisconsin-Madison, Madison, Wisconsin

NASA Technical Reports Server (NTRS)

1985-01-01

Operational forecasters have habitually been plagued with the problems associated with acquisition, display, and dissemination of data used in preparing forecasts. The centralized storm information system (CSIS) experiment provided an operational forecaster with an interactive computer system which could perform these preliminary tasks more quickly and accurately than any human could. CSIS objectives pertaining to improved severe storms forecasting and warning procedures are addressed.

The Implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for Global Dust Forecasting at NOAA NCEP

NASA Technical Reports Server (NTRS)

Lu, Cheng-Hsuan; Da Silva, Arlindo M.; Wang, Jun; Moorthi, Shrinivas; Chin, Mian; Colarco, Peter; Tang, Youhua; Bhattacharjee, Partha S.; Chen, Shen-Po; Chuang, Hui-Ya;

NOMADS-NOAA Operational Model Archive and Distribution System

Science.gov Websites

Forecast Maps Climate Climate Prediction Climate Archives Weather Safety Storm Ready NOAA Central Library (16km) 6 hours grib filter http OpenDAP-alt URMA hourly - http - Climate Models Climate Forecast System Flux Products 6 hours grib filter http - Climate Forecast System 3D Pressure Products 6 hours grib

Impacts of Short-Term Solar Power Forecasts in System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibanez, Eduardo; Krad, Ibrahim; Hodge, Bri-Mathias

2016-05-05

Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-daymore » operations.« less

The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

NASA Technical Reports Server (NTRS)

Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

2014-01-01

The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the ExREF in preparing their rainfall forecasts. Preliminary results will be presented.

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

NASA Astrophysics Data System (ADS)

Hou, Tuanjie; Kong, Fanyou; Chen, Xunlai; Lei, Hengchi; Hu, Zhaoxia

2015-07-01

To improve the accuracy of short-term (0-12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System (HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) three-dimensional variational data assimilation (3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station (AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting (QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score (FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.

Oregon Washington Coastal Ocean Forecast System: Real-time Modeling and Data Assimilation

NASA Astrophysics Data System (ADS)

Erofeeva, S.; Kurapov, A. L.; Pasmans, I.

2016-02-01

Three-day forecasts of ocean currents, temperature and salinity along the Oregon and Washington coasts are produced daily by a numerical ROMS-based ocean circulation model. NAM is used to derive atmospheric forcing for the model. Fresh water discharge from Columbia River, Fraser River, and small rivers in Puget Sound are included. The forecast is constrained by open boundary conditions derived from the global Navy HYCOM model and once in 3 days assimilation of recent data, including HF radar surface currents, sea surface temperature from the GOES satellite, and SSH from several satellite altimetry missions. 4-dimensional variational data assimilation is implemented in 3-day time windows using the tangent linear and adjoint codes developed at OSU. The system is semi-autonomous - all the data, including NAM and HYCOM fields are automatically updated, and daily operational forecast is automatically initiated. The pre-assimilation data quality control and post-assimilation forecast quality control require the operator's involvement. The daily forecast and 60 days of hindcast fields are available for public on opendap. As part of the system model validation plots to various satellites and SEAGLIDER are also automatically updated and available on the web (http://ingria.coas.oregonstate.edu/rtdavow/). Lessons learned in this pilot real-time coastal ocean forecasting project help develop and test metrics for forecast skill assessment for the West Coast Operational Forecast System (WCOFS), currently at testing and development phase at the National Oceanic and Atmospheric Administration (NOAA).

An expert system-based approach to prediction of year-to-year climatic variations in the North Atlantic region

NASA Astrophysics Data System (ADS)

Rodionov, S. N.; Martin, J. H.

1999-07-01

A novel approach to climate forecasting on an interannual time scale is described. The approach is based on concepts and techniques from artificial intelligence and expert systems. The suitability of this approach to climate diagnostics and forecasting problems and its advantages compared with conventional forecasting techniques are discussed. The article highlights some practical aspects of the development of climatic expert systems (CESs) and describes an implementation of such a system for the North Atlantic (CESNA). Particular attention is paid to the content of CESNA's knowledge base and those conditions that make climatic forecasts one to several years in advance possible. A detailed evaluation of the quality of the experimental real-time forecasts made by CESNA for the winters of 1995-1996, 1996-1997 and 1997-1998 are presented.

Stochastic Forcing for High-Resolution Regional and Global Ocean and Atmosphere-Ocean Coupled Ensemble Forecast System

NASA Astrophysics Data System (ADS)

Rowley, C. D.; Hogan, P. J.; Martin, P.; Thoppil, P.; Wei, M.

2017-12-01

An extended range ensemble forecast system is being developed in the US Navy Earth System Prediction Capability (ESPC), and a global ocean ensemble generation capability to represent uncertainty in the ocean initial conditions has been developed. At extended forecast times, the uncertainty due to the model error overtakes the initial condition as the primary source of forecast uncertainty. Recently, stochastic parameterization or stochastic forcing techniques have been applied to represent the model error in research and operational atmospheric, ocean, and coupled ensemble forecasts. A simple stochastic forcing technique has been developed for application to US Navy high resolution regional and global ocean models, for use in ocean-only and coupled atmosphere-ocean-ice-wave ensemble forecast systems. Perturbation forcing is added to the tendency equations for state variables, with the forcing defined by random 3- or 4-dimensional fields with horizontal, vertical, and temporal correlations specified to characterize different possible kinds of error. Here, we demonstrate the stochastic forcing in regional and global ensemble forecasts with varying perturbation amplitudes and length and time scales, and assess the change in ensemble skill measured by a range of deterministic and probabilistic metrics.

Alternative Approaches to Land Initialization for Seasonal Precipitation and Temperature Forecasts

NASA Technical Reports Server (NTRS)

Koster, Randal; Suarez, Max; Liu, Ping; Jambor, Urszula

2004-01-01

The seasonal prediction system of the NASA Global Modeling and Assimilation Office is used to generate ensembles of summer forecasts utilizing realistic soil moisture initialization. To derive the realistic land states, we drive offline the system's land model with realistic meteorological forcing over the period 1979-1993 (in cooperation with the Global Land Data Assimilation System project at GSFC) and then extract the state variables' values on the chosen forecast start dates. A parallel series of forecast ensembles is performed with a random (though climatologically consistent) set of land initial conditions; by comparing the two sets of ensembles, we can isolate the impact of land initialization on forecast skill from that of the imposed SSTs. The base initialization experiment is supplemented with several forecast ensembles that use alternative initialization techniques. One ensemble addresses the impact of minimizing climate drift in the system through the scaling of the initial conditions, and another is designed to isolate the importance of the precipitation signal from that of all other signals in the antecedent offline forcing. A third ensemble includes a more realistic initialization of the atmosphere along with the land initialization. The impact of each variation on forecast skill is quantified.

On the reliability of seasonal climate forecasts

PubMed Central

Weisheimer, A.; Palmer, T. N.

2014-01-01

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time. PMID:24789559

A scenario and forecast model for Gulf of Mexico hypoxic area and volume

USGS Publications Warehouse

Scavia, Donald; Evans, Mary Anne; Obenour, Daniel R.

2013-01-01

For almost three decades, the relative size of the hypoxic region on the Louisiana-Texas continental shelf has drawn scientific and policy attention. During that time, both simple and complex models have been used to explore hypoxia dynamics and to provide management guidance relating the size of the hypoxic zone to key drivers. Throughout much of that development, analyses had to accommodate an apparent change in hypoxic sensitivity to loads and often cull observations due to anomalous meteorological conditions. Here, we describe an adaptation of our earlier, simple biophysical model, calibrated to revised hypoxic area estimates and new hypoxic volume estimates through Bayesian estimation. This application eliminates the need to cull observations and provides revised hypoxic extent estimates with uncertainties, corresponding to different nutrient loading reduction scenarios. We compare guidance from this model application, suggesting an approximately 62% nutrient loading reduction is required to reduce Gulf hypoxia to the Action Plan goal of 5,000 km2, to that of previous applications. In addition, we describe for the first time, the corresponding response of hypoxic volume. We also analyze model results to test for increasing system sensitivity to hypoxia formation, but find no strong evidence of such change.

Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

NASA Astrophysics Data System (ADS)

Bao, Hongjun; Zhao, Linna

2012-02-01

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.

Is the economic value of hydrological forecasts related to their quality? Case study of the hydropower sector.

NASA Astrophysics Data System (ADS)

Cassagnole, Manon; Ramos, Maria-Helena; Thirel, Guillaume; Gailhard, Joël; Garçon, Rémy

2017-04-01

The improvement of a forecasting system and the evaluation of the quality of its forecasts are recurrent steps in operational practice. However, the evaluation of forecast value or forecast usefulness for better decision-making is, to our knowledge, less frequent, even if it might be essential in many sectors such as hydropower and flood warning. In the hydropower sector, forecast value can be quantified by the economic gain obtained with the optimization of operations or reservoir management rules. Several hydropower operational systems use medium-range forecasts (up to 7-10 days ahead) and energy price predictions to optimize hydropower production. Hence, the operation of hydropower systems, including the management of water in reservoirs, is impacted by weather, climate and hydrologic variability as well as extreme events. In order to assess how the quality of hydrometeorological forecasts impact operations, it is essential to first understand if and how operations and management rules are sensitive to input predictions of different quality. This study investigates how 7-day ahead deterministic and ensemble streamflow forecasts of different quality might impact the economic gains of energy production. It is based on a research model developed by Irstea and EDF to investigate issues relevant to the links between quality and value of forecasts in the optimisation of energy production at the short range. Based on streamflow forecasts and pre-defined management constraints, the model defines the best hours (i.e., the hours with high energy prices) to produce electricity. To highlight the link between forecasts quality and their economic value, we built several synthetic ensemble forecasts based on observed streamflow time series. These inputs are generated in a controlled environment in order to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts are used to assess the sensitivity of the decision model to forecast quality. Relationships between forecast quality and economic value are discussed. This work is part of the IMPREX project, a research project supported by the European Commission under the Horizon 2020 Framework programme, with grant No. 641811 (http://www.imprex.eu)

The 30/20 GHZ net market assessment

NASA Technical Reports Server (NTRS)

Rogers, J. C.; Reiner, P.

1980-01-01

By creating a number of market scenarios variations dealing with network types, network sizes, and service price levels were analyzed for their impact on market demand. Each market scenario represents a market demand forecast with results for voice, data, and video service traffic expressed in peak load megabits per second.

Wave ensemble forecast system for tropical cyclones in the Australian region

NASA Astrophysics Data System (ADS)

Zieger, Stefan; Greenslade, Diana; Kepert, Jeffrey D.

2018-05-01

Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4∘) and regional (resolution 1/10∘) domains with forecast ranges of + 7 and + 3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing + 10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events.

2006 Pacific Northwest Loads and Resources Study.

DOE Office of Scientific and Technical Information (OSTI.GOV)

United States. Bonneville Power Administration.

2006-03-01

The Pacific Northwest Loads and Resources Study (White Book), which is published annually by the Bonneville Power Administration (BPA), establishes one of the planning bases for supplying electricity to customers. The White Book contains projections of regional and Federal system load and resource capabilities, along with relevant definitions and explanations. The White Book also contains information obtained from formalized resource planning reports and data submittals including those from individual utilities, the Northwest Power and Conservation Council (Council), and the Pacific Northwest Utilities Conference Committee (PNUCC). The White Book is not an operational planning guide, nor is it used for determiningmore » BPA revenues, although the database that generates the data for the White Book analysis contributes to the development of BPA's inventory and ratemaking processes. Operation of the Federal Columbia River Power System (FCRPS) is based on a set of criteria different from that used for resource planning decisions. Operational planning is dependent upon real-time or near-term knowledge of system conditions that include expectations of river flows and runoff, market opportunities, availability of reservoir storage, energy exchanges, and other factors affecting the dynamics of operating a power system. The load resource balance of both the Federal system and the region is determined by comparing resource availability to an expected level of total retail electricity consumption. Resources include projected energy capability plus contract purchases. Loads include a forecast of retail obligations plus contract obligations. Surplus energy is available when resources are greater than loads. This surplus energy could be marketed to increase revenues. Energy deficits occur when resources are less than loads. These energy deficits will be met by any combination of the following: better-than-critical water conditions, demand-side management and conservation programs, permanent loss of loads due to economic conditions or closures, additional contract purchases, and/or the addition of new generating resources. This study incorporates information on Pacific Northwest (PNW) regional retail loads, contract obligations, and contract resources. This loads and resources analysis simulates the operation of the power system in the PNW. The simulated hydro operation incorporates plant characteristics, streamflows, and non-power requirements from the current Pacific Northwest Coordination Agreement (PNCA). Additional resource capability estimates were provided by BPA, PNW Federal agency, public agency, cooperative, U.S. Bureau of Reclamation (USBR), and investor-owned utility (IOU) customers furnished through annual PNUCC data submittals for 2005 and/or direct submittals to BPA. The 2006 White Book is presented in two documents: (1) this summary document of Federal system and PNW region loads and resources, and (2) a technical appendix which presents regional loads, grouped by major PNW utility categories, and detailed contract and resource information. The technical appendix is available only in electronic form. Individual customer information for marketer contracts is not detailed due to confidentiality agreements. The 2006 White Book analysis updates the 2004 White Book. This analysis shows projections of the Federal system and region's yearly average annual energy consumption and resource availability for the study period, OY 2007-2016. The study also presents projections of Federal system and region expected 1-hour monthly peak demand, monthly energy demand, monthly 1-hour peak generating capability, and monthly energy generation for OY 2007, 2011, and 2016. BPA is investigating a new approach in capacity planning depicting the monthly Federal system 120-hour peak generating capability and 120-hour peak surplus/deficit for OY 2007, 2011, and 2016. This document analyzes the PNW's projected loads and available generating resources in two parts: (1) the loads and resources of the Federal system, for which BPA is the marketing agency; and (2) the larger PNW regional power system loads and resources that include the Federal system as well other PNW entities.« less

Worldwide satellite market demand forecast

NASA Technical Reports Server (NTRS)

Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

1981-01-01

The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.

Current and future data assimilation development in the Copernicus Atmosphere Monitoring Service

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Ades, M.; Agusti-panareda, A.; Flemming, J.; Inness, A.; Kipling, Z.; Parrington, M.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The system assimilates observations from more than 60 satellite sensors to constrain both the meteorology and the atmospheric composition species. While an operational forecasting system needs to be robust and reliable, it also needs to stay state-of-the-art to provide the best possible forecasts. Continuous development is therefore an important component of the CAMS systems. We will present on-going efforts on improving the 4D-Var data assimilation system, such as using ensemble data assimilation to improve the background error covariances and more accurate use of satellite observations. We will also outline plans for including emissions in the daily CAMS analyses, which is an area where research activities have a large potential to feed into operational applications.

Worldwide satellite market demand forecast

NASA Astrophysics Data System (ADS)

Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

1981-06-01

The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.

Decision Support on the Sediments Flushing of Aimorés Dam Using Medium-Range Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Schwanenberg, Dirk; Collischonn, Walter; Assis dos Reis, Alberto; Alvarado Montero, Rodolfo; Alencar Siqueira, Vinicius

2015-04-01

In the present study we investigate the use of medium-range streamflow forecasts in the Doce River basin (Brazil), at the reservoir of Aimorés Hydro Power Plant (HPP). During daily operations this reservoir acts as a "trap" to the sediments that originate from the upstream basin of the Doce River. This motivates a cleaning process called "pass through" to periodically remove the sediments from the reservoir. The "pass through" or "sediments flushing" process consists of a decrease of the reservoir's water level to a certain flushing level when a determined reservoir inflow threshold is forecasted. Then, the water in the approaching inflow is used to flush the sediments from the reservoir through the spillway and to recover the original reservoir storage. To be triggered, the sediments flushing operation requires an inflow larger than 3000m³/s in a forecast horizon of 7 days. This lead-time of 7 days is far beyond the basin's concentration time (around 2 days), meaning that the forecasts for the pass through procedure highly depends on Numerical Weather Predictions (NWP) models that generate Quantitative Precipitation Forecasts (QPF). This dependency creates an environment with a high amount of uncertainty to the operator. To support the decision making at Aimorés HPP we developed a fully operational hydrological forecasting system to the basin. The system is capable of generating ensemble streamflow forecasts scenarios when driven by QPF data from meteorological Ensemble Prediction Systems (EPS). This approach allows accounting for uncertainties in the NWP at a decision making level. This system is starting to be used operationally by CEMIG and is the one shown in the present study, including a hindcasting analysis to assess the performance of the system for the specific flushing problem. The QPF data used in the hindcasting study was derived from the TIGGE (THORPEX Interactive Grand Global Ensemble) database. Among all EPS available on TIGGE, three were selected: ECMWF, GEFS, and CPTEC. As a deterministic reference forecast, we adopt the high resolution ECMWF forecast for comparison. The experiment consisted on running retrospective forecasts for a full five-year period. To verify the proposed objectives of the study, we use different metrics to evaluate the forecast: ROC Curves, Exceedance Diagrams, Forecast Convergence Score (FCS). Metrics results enabled to understand the benefits of the hydrological ensemble prediction system as a decision making tool for the HPP operation. The ROC scores indicate that the use of the lower percentiles of the ensemble scenarios issues for a true alarm rate around 0,5 to 0,8 (depending on the model and on the percentile), for the lead time of seven days. While the false alarm rate is between 0 and 0,3. Those rates were better than the ones resulting from the deterministic reference forecast. Exceedance diagrams and forecast convergence scores indicate that the ensemble scenarios provide an early signal about the threshold crossing. Furthermore, the ensemble forecasts are more consistent between two subsequent forecasts in comparison to the deterministic forecast. The assessments results also give more credibility to CEMIG in the realization and communication of flushing operation with the stakeholders involved.

Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model

PubMed Central

Zhang, Jinlun

2015-01-01

Abstract Arctic sea ice drift forecasts of 6 h–9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h–8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high‐resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24–48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km × 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast. PMID:27818852

A system for forecasting and monitoring cash flow : phase I : forecasting payments on construction contracts.

DOT National Transportation Integrated Search

1983-01-01

The research on which this paper is based was performed as part of a study to develop a system for generating a one-to-two year forecast of monthly cash flows for the Virginia Department of Highways and Transportation. It revealed that presently used...

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solarmore » power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.« less

Coastal and Riverine Flood Forecast Model powered by ADCIRC

NASA Astrophysics Data System (ADS)

Khalid, A.; Ferreira, C.

2017-12-01

Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which might provide better and more reliable forecast for the flood affected communities.

Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parks, K.; Wan, Y. H.; Wiener, G.

2011-10-01

The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'),more » or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are higher. In organized electricity markets, units that are committed for reliability reasons are paid their offer price even when prevailing market prices are lower. Often, these uplift charges are allocated to market participants that caused the inefficient dispatch in the first place. Thus, wind energy facilities are burdened with their share of costs proportional to their forecast errors. For Xcel Energy, wind energy uncertainty costs manifest depending on specific market structures. In the Public Service of Colorado (PSCo), inefficient commitment and dispatch caused by wind uncertainty increases fuel costs. Wind resources participating in the Midwest Independent System Operator (MISO) footprint make substantial payments in the real-time markets to true-up their day-ahead positions and are additionally burdened with deviation charges called a Revenue Sufficiency Guarantee (RSG) to cover out of market costs associated with operations. Southwest Public Service (SPS) wind plants cause both commitment inefficiencies and are charged Southwest Power Pool (SPP) imbalance payments due to wind uncertainty and variability. Wind energy forecasting helps mitigate these costs. Wind integration studies for the PSCo and Northern States Power (NSP) operating companies have projected increasing costs as more wind is installed on the system due to forecast error. It follows that reducing forecast error would reduce these costs. This is echoed by large scale studies in neighboring regions and states that have recommended adoption of state-of-the-art wind forecasting tools in day-ahead and real-time planning and operations. Further, Xcel Energy concluded reduction of the normalized mean absolute error by one percent would have reduced costs in 2008 by over $1 million annually in PSCo alone. The value of reducing forecast error prompted Xcel Energy to make substantial investments in wind energy forecasting research and development.« less

Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

2016-12-01

The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.

Appropriateness in using LANDSAT in development energy related data bases

NASA Technical Reports Server (NTRS)

Harnden, E.

1981-01-01

The use of automated classification systems in the field of resource management and resource inventory is discussed. Applications of LANDSAT classification are outlined and include: energy load forecasting based upon land use inventories and change analysis, impact analysis of activities related to energy extraction, capability/suitability mapping in support of generation and substation location and transmission line routing, and assessment of solar energy potential in a highly urbanized setting where land values are high. It is found that the use of LANDSAT data is adequate for general inventories where few data categories are required, where resolution of data to around 150 acres minimum is required, and where no other complete imagery set can be obtained.

Proceedings of the American Power Conference. Volume 58-I

DOE Office of Scientific and Technical Information (OSTI.GOV)

McBride, A.E.

1996-10-01

This is volume 58-I of the proceedings of the American Power Conference, 1996, Technology for Competition and Globalization. The topics of the papers include power plant DC issues; cost of environmental compliance; advanced coal systems -- environmental performance; technology for competition in dispersed generation; superconductivity technologies for electric utility applications; power generation trends and challenges in China; aging in nuclear power plants; innovative and competitive repowering options; structural examinations, modifications and repairs; electric load forecasting; distribution planning; EMF effects; fuzzy logic and neural networks for power plant applications; electrokinetic decontamination of soils; integrated gasification combined cycle; advances in fusion; coolingmore » towers; relays; plant controls; flue gas desulfurization; waste product utilization; and improved technologies.« less

Filling the Gaps: The Synergistic Application of Satellite Data for the Volcanic Ash Threat to Aviation

NASA Technical Reports Server (NTRS)

Murray, John; Vernier, Jean-Paul; Fairlie, T. Duncan; Pavolonis, Michael; Krotkov, Nickolay A.; Lindsay, Francis; Haynes, John

2013-01-01

Although significant progress has been made in recent years, estimating volcanic ash concentration for the full extent of the airspace affected by volcanic ash remains a challenge. No single satellite, airborne or ground observing system currently exists which can sufficiently inform dispersion models to provide the degree of accuracy required to use them with a high degree of confidence for routing aircraft in and near volcanic ash. Toward this end, the detection and characterization of volcanic ash in the atmosphere may be substantially improved by integrating a wider array of observing systems and advancements in trajectory and dispersion modeling to help solve this problem. The qualitative aspect of this effort has advanced significantly in the past decade due to the increase of highly complementary observational and model data currently available. Satellite observations, especially when coupled with trajectory and dispersion models can provide a very accurate picture of the 3-dimensional location of ash clouds. The accurate estimate of the mass loading at various locations throughout the entire plume, however improving, remains elusive. This paper examines the capabilities of various satellite observation systems and postulates that model-based volcanic ash concentration maps and forecasts might be significantly improved if the various extant satellite capabilities are used together with independent, accurate mass loading data from other observing systems available to calibrate (tune) ash concentration retrievals from the satellite systems.

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Regional Air Quality forecAST (RAQAST) Over the U.S

NASA Astrophysics Data System (ADS)

Yoshida, Y.; Choi, Y.; Zeng, T.; Wang, Y.

2005-12-01

A regional chemistry and transport modeling system is used to provide 48-hour forecast of the concentrations of ozone and its precursors over the United States. Meteorological forecast is conducted using the NCAR/Penn State MM5 model. The regional chemistry and transport model simulates the sources, transport, chemistry, and deposition of 24 chemical tracers. The lateral and upper boundary conditions of trace gas concentrations are specified using the monthly mean output from the global GEOS-CHEM model. The initial and boundary conditions for meteorological fields are taken from the NOAA AVN forecast. The forecast has been operational since August, 2003. Model simulations are evaluated using surface, aircraft, and satellite measurements in the A'hindcast' mode. The next step is an automated forecast evaluation system.

A Scheme for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

DTIC Science & Technology

1984-07-01

D.A. Lowry, 1978: Use of a real - time computer graphics system for diagnosis and forecasting . Preprints, Conf. on Wes. Forecasting and Analysis and...28 Figure 4.2.1. Graph for forecasting the night minimum temperature from observations at 1800-2000 local time . From Zverev (1972...3u 1. 2 much weather is produced by organized systems that translate, and forecast gains were made through use of the concepts of steering

An experimental system for flood risk forecasting at global scale

NASA Astrophysics Data System (ADS)

Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.

2016-12-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.

iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Ferreira, C.

2017-12-01

Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system simulations will help to develop a seamless integration with the boundary systems in the service-gap area with new insights into our scientific understanding of such complex systems. A visualization system is being developed to allow stake holders and the community to have access to the flood forecasting for their region with sufficient lead time.

Operational Earthquake Forecasting of Aftershocks for New England

NASA Astrophysics Data System (ADS)

Ebel, J.; Fadugba, O. I.

2015-12-01

Although the forecasting of mainshocks is not possible, recent research demonstrates that probabilistic forecasts of expected aftershock activity following moderate and strong earthquakes is possible. Previous work has shown that aftershock sequences in intraplate regions behave similarly to those in California, and thus the operational aftershocks forecasting methods that are currently employed in California can be adopted for use in areas of the eastern U.S. such as New England. In our application, immediately after a felt earthquake in New England, a forecast of expected aftershock activity for the next 7 days will be generated based on a generic aftershock activity model. Approximately 24 hours after the mainshock, the parameters of the aftershock model will be updated using the observed aftershock activity observed to that point in time, and a new forecast of expected aftershock activity for the next 7 days will be issued. The forecast will estimate the average number of weak, felt aftershocks and the average expected number of aftershocks based on the aftershock statistics of past New England earthquakes. The forecast also will estimate the probability that an earthquake that is stronger than the mainshock will take place during the next 7 days. The aftershock forecast will specify the expected aftershocks locations as well as the areas over which aftershocks of different magnitudes could be felt. The system will use web pages, email and text messages to distribute the aftershock forecasts. For protracted aftershock sequences, new forecasts will be issued on a regular basis, such as weekly. Initially, the distribution system of the aftershock forecasts will be limited, but later it will be expanded as experience with and confidence in the system grows.

The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freedman, Jeffrey M.; Manobianco, John; Schroeder, John

This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10more » - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.« less

Constraints on Rational Model Weighting, Blending and Selecting when Constructing Probability Forecasts given Multiple Models

NASA Astrophysics Data System (ADS)

Higgins, S. M. W.; Du, H. L.; Smith, L. A.

2012-04-01

Ensemble forecasting on a lead time of seconds over several years generates a large forecast-outcome archive, which can be used to evaluate and weight "models". Challenges which arise as the archive becomes smaller are investigated: in weather forecasting one typically has only thousands of forecasts however those launched 6 hours apart are not independent of each other, nor is it justified to mix seasons with different dynamics. Seasonal forecasts, as from ENSEMBLES and DEMETER, typically have less than 64 unique launch dates; decadal forecasts less than eight, and long range climate forecasts arguably none. It is argued that one does not weight "models" so much as entire ensemble prediction systems (EPSs), and that the marginal value of an EPS will depend on the other members in the mix. The impact of using different skill scores is examined in the limits of both very large forecast-outcome archives (thereby evaluating the efficiency of the skill score) and in very small forecast-outcome archives (illustrating fundamental limitations due to sampling fluctuations and memory in the physical system being forecast). It is shown that blending with climatology (J. Bröcker and L.A. Smith, Tellus A, 60(4), 663-678, (2008)) tends to increase the robustness of the results; also a new kernel dressing methodology (simply insuring that the expected probability mass tends to lie outside the range of the ensemble) is illustrated. Fair comparisons using seasonal forecasts from the ENSEMBLES project are used to illustrate the importance of these results with fairly small archives. The robustness of these results across the range of small, moderate and huge archives is demonstrated using imperfect models of perfectly known nonlinear (chaotic) dynamical systems. The implications these results hold for distinguishing the skill of a forecast from its value to a user of the forecast are discussed.

Validation of Seasonal Forecast of Indian Summer Monsoon Rainfall

NASA Astrophysics Data System (ADS)

Das, Sukanta Kumar; Deb, Sanjib Kumar; Kishtawal, C. M.; Pal, Pradip Kumar

2015-06-01

The experimental seasonal forecast of Indian summer monsoon (ISM) rainfall during June through September using Community Atmosphere Model (CAM) version 3 has been carried out at the Space Applications Centre Ahmedabad since 2009. The forecasts, based on a number of ensemble members (ten minimum) of CAM, are generated in several phases and updated on regular basis. On completion of 5 years of experimental seasonal forecasts in operational mode, it is required that the overall validation or correctness of the forecast system is quantified and that the scope is assessed for further improvements of the forecast over time, if any. The ensemble model climatology generated by a set of 20 identical CAM simulations is considered as the model control simulation. The performance of the forecast has been evaluated by assuming the control simulation as the model reference. The forecast improvement factor shows positive improvements, with higher values for the recent forecasted years as compared to the control experiment over the Indian landmass. The Taylor diagram representation of the Pearson correlation coefficient (PCC), standard deviation and centered root mean square difference has been used to demonstrate the best PCC, in the order of 0.74-0.79, recorded for the seasonal forecast made during 2013. Further, the bias score of different phases of experiment revealed the fact that the ISM rainfall forecast is affected by overestimation in predicting the low rain-rate (less than 7 mm/day), but by underestimation in the medium and high rain-rate (higher than 11 mm/day). Overall, the analysis shows significant improvement of the ISM forecast over the last 5 years, viz. 2009-2013, due to several important modifications that have been implemented in the forecast system. The validation exercise has also pointed out a number of shortcomings in the forecast system; these will be addressed in the upcoming years of experiments to improve the quality of the ISM prediction.

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

NASA Technical Reports Server (NTRS)

Barghouty, Nasser; Falconer, David

2015-01-01

Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output