A genetic-algorithm-based remnant grey prediction model for energy demand forecasting

PubMed Central

2017-01-01

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants. PMID:28981548

Forecasting urban water demand: A meta-regression analysis.

PubMed

Sebri, Maamar

2016-12-01

Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

Development of speed models for improving travel forecasting and highway performance evaluation : [technical summary].

DOT National Transportation Integrated Search

2013-12-01

Travel forecasting models predict travel demand based on the present transportation system and its use. Transportation modelers must develop, validate, and calibrate models to ensure that predicted travel demand is as close to reality as possible. Mo...

Gas demand forecasting by a new artificial intelligent algorithm

NASA Astrophysics Data System (ADS)

Khatibi. B, Vahid; Khatibi, Elham

2012-01-01

Energy demand forecasting is a key issue for consumers and generators in all energy markets in the world. This paper presents a new forecasting algorithm for daily gas demand prediction. This algorithm combines a wavelet transform and forecasting models such as multi-layer perceptron (MLP), linear regression or GARCH. The proposed method is applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the proposed method.

Improving wave forecasting by integrating ensemble modelling and machine learning

NASA Astrophysics Data System (ADS)

O'Donncha, F.; Zhang, Y.; James, S. C.

2017-12-01

Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

Forecasting in foodservice: model development, testing, and evaluation.

PubMed

Miller, J L; Thompson, P A; Orabella, M M

1991-05-01

This study was designed to develop, test, and evaluate mathematical models appropriate for forecasting menu-item production demand in foodservice. Data were collected from residence and dining hall foodservices at Ohio State University. Objectives of the study were to collect, code, and analyze the data; develop and test models using actual operation data; and compare forecasting results with current methods in use. Customer count was forecast using deseasonalized simple exponential smoothing. Menu-item demand was forecast by multiplying the count forecast by a predicted preference statistic. Forecasting models were evaluated using mean squared error, mean absolute deviation, and mean absolute percentage error techniques. All models were more accurate than current methods. A broad spectrum of forecasting techniques could be used by foodservice managers with access to a personal computer and spread-sheet and database-management software. The findings indicate that mathematical forecasting techniques may be effective in foodservice operations to control costs, increase productivity, and maximize profits.

Demand forecasting of electricity in Indonesia with limited historical data

NASA Astrophysics Data System (ADS)

Dwi Kartikasari, Mujiati; Rohmad Prayogi, Arif

2018-03-01

Demand forecasting of electricity is an important activity for electrical agents to know the description of electricity demand in future. Prediction of demand electricity can be done using time series models. In this paper, double moving average model, Holt’s exponential smoothing model, and grey model GM(1,1) are used to predict electricity demand in Indonesia under the condition of limited historical data. The result shows that grey model GM(1,1) has the smallest value of MAE (mean absolute error), MSE (mean squared error), and MAPE (mean absolute percentage error).

Transportation Sector Model of the National Energy Modeling System. Volume 1

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

NONE

1998-01-01

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. The NEMS Transportation Model comprises a series of semi-independent models which address different aspects of the transportation sector. The primary purpose of this model is to provide mid-term forecasts of transportation energy demand by fuel type including, but not limited to, motor gasoline, distillate, jet fuel, and alternative fuels (such as CNG) not commonly associated with transportation. Themore » current NEMS forecast horizon extends to the year 2010 and uses 1990 as the base year. Forecasts are generated through the separate consideration of energy consumption within the various modes of transport, including: private and fleet light-duty vehicles; aircraft; marine, rail, and truck freight; and various modes with minor overall impacts, such as mass transit and recreational boating. This approach is useful in assessing the impacts of policy initiatives, legislative mandates which affect individual modes of travel, and technological developments. The model also provides forecasts of selected intermediate values which are generated in order to determine energy consumption. These elements include estimates of passenger travel demand by automobile, air, or mass transit; estimates of the efficiency with which that demand is met; projections of vehicle stocks and the penetration of new technologies; and estimates of the demand for freight transport which are linked to forecasts of industrial output. Following the estimation of energy demand, TRAN produces forecasts of vehicular emissions of the following pollutants by source: oxides of sulfur, oxides of nitrogen, total carbon, carbon dioxide, carbon monoxide, and volatile organic compounds.« less

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.

New product forecasting with limited or no data

NASA Astrophysics Data System (ADS)

Ismai, Zuhaimy; Abu, Noratikah; Sufahani, Suliadi

2016-10-01

In the real world, forecasts would always be based on historical data with the assumption that the behaviour be the same for the future. But how do we forecast when there is no such data available? New product or new technologies normally has limited amount of data available. Knowing that forecasting is valuable for decision making, this paper presents forecasting of new product or new technologies using aggregate diffusion models and modified Bass Model. A newly launched Proton car and its penetration was chosen to demonstrate the possibility of forecasting sales demand where there is limited or no data available. The model was developed to forecast diffusion of new vehicle or an innovation in the Malaysian society. It is to represent the level of spread on the new vehicle among a given set of the society in terms of a simple mathematical function that elapsed since the introduction of the new product. This model will forecast the car sales volume. A procedure of the proposed diffusion model was designed and the parameters were estimated. Results obtained by applying the proposed diffusion model and numerical calculation shows that the model is robust and effective for forecasting demand of the new vehicle. The results reveal that newly developed modified Bass diffusion of demand function has significantly contributed for forecasting the diffusion of new Proton car or new product.

Satellite provided customer premises services: A forecast of potential domestic demand through the year 2000. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Development of a forecast of the total domestic telecommunications demand, identification of that portion of the telecommunications demand suitable for transmission by satellite systems, identification of that portion of the satellite market addressable by CPS systems, identification of that portion of the satellite market addressable by Ka-band CPS system, and postulation of a Ka-band CPS network on a nationwide and local level were achieved. The approach employed included the use of a variety of forecasting models, a parametric cost model, a market distribution model and a network optimization model. Forecasts were developed for: 1980, 1990, 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

Satellite provided customer premises services: A forecast of potential domestic demand through the year 2000. Volume: Executive summary

NASA Astrophysics Data System (ADS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-08-01

Development of a forecast of the total domestic telecommunications demand, identification of that portion of the telecommunications demand suitable for transmission by satellite systems, identification of that portion of the satellite market addressable by CPS systems, identification of that portion of the satellite market addressable by Ka-band CPS system, and postulation of a Ka-band CPS network on a nationwide and local level were achieved. The approach employed included the use of a variety of forecasting models, a parametric cost model, a market distribution model and a network optimization model. Forecasts were developed for: 1980, 1990, 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

Multimodal Transportation Analysis Process (MTAP): A Travel Demand Forecasting Model

DOT National Transportation Integrated Search

1990-01-01

In 1986, the North Central Texas Council of Governments (NCTCOG) undertook the revision of its travel demand forecasting model. The outcome was a model which was developed based on travel patterns in the Dallas-Forth Worth area and used jointly by th...

Performance of time-series methods in forecasting the demand for red blood cell transfusion.

PubMed

Pereira, Arturo

2004-05-01

Planning the future blood collection efforts must be based on adequate forecasts of transfusion demand. In this study, univariate time-series methods were investigated for their performance in forecasting the monthly demand for RBCs at one tertiary-care, university hospital. Three time-series methods were investigated: autoregressive integrated moving average (ARIMA), the Holt-Winters family of exponential smoothing models, and one neural-network-based method. The time series consisted of the monthly demand for RBCs from January 1988 to December 2002 and was divided into two segments: the older one was used to fit or train the models, and the younger to test for the accuracy of predictions. Performance was compared across forecasting methods by calculating goodness-of-fit statistics, the percentage of months in which forecast-based supply would have met the RBC demand (coverage rate), and the outdate rate. The RBC transfusion series was best fitted by a seasonal ARIMA(0,1,1)(0,1,1)(12) model. Over 1-year time horizons, forecasts generated by ARIMA or exponential smoothing laid within the +/- 10 percent interval of the real RBC demand in 79 percent of months (62% in the case of neural networks). The coverage rate for the three methods was 89, 91, and 86 percent, respectively. Over 2-year time horizons, exponential smoothing largely outperformed the other methods. Predictions by exponential smoothing laid within the +/- 10 percent interval of real values in 75 percent of the 24 forecasted months, and the coverage rate was 87 percent. Over 1-year time horizons, predictions of RBC demand generated by ARIMA or exponential smoothing are accurate enough to be of help in the planning of blood collection efforts. For longer time horizons, exponential smoothing outperforms the other forecasting methods.

Examination of simplified travel demand model. [Internal volume forecasting model

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

Smith, R.L. Jr.; McFarlane, W.J.

1978-01-01

A simplified travel demand model, the Internal Volume Forecasting (IVF) model, proposed by Low in 1972 is evaluated as an alternative to the conventional urban travel demand modeling process. The calibration of the IVF model for a county-level study area in Central Wisconsin results in what appears to be a reasonable model; however, analysis of the structure of the model reveals two primary mis-specifications. Correction of the mis-specifications leads to a simplified gravity model version of the conventional urban travel demand models. Application of the original IVF model to ''forecast'' 1960 traffic volumes based on the model calibrated for 1970more » produces accurate estimates. Shortcut and ad hoc models may appear to provide reasonable results in both the base and horizon years; however, as shown by the IVF mode, such models will not always provide a reliable basis for transportation planning and investment decisions.« less

Approaches to Forecasting Demands for Library Network Services. Report No. 10.

ERIC Educational Resources Information Center

Kang, Jong Hoa

The problem of forecasting monthly demands for library network services is considered in terms of using forecasts as inputs to policy analysis models, and in terms of using forecasts to aid in the making of budgeting and staffing decisions. Box-Jenkins time-series methodology, adaptive filtering, and regression approaches are examined and compared…

Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Short-Term Energy Outlook Supplement March 1998)

EIA Publications

1998-01-01

The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.

Short-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

2018-04-01

Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

Model documentation report: Residential sector demand module of the national energy modeling system

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

NONE

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This reference document provides a detailed description for energy analysts, other users, and the public. The NEMS Residential Sector Demand Module is currently used for mid-term forecasting purposes and energy policy analysis over the forecast horizon of 1993 through 2020. The model generates forecasts of energy demand for the residential sector by service, fuel, and Census Division. Policy impacts resulting from new technologies,more » market incentives, and regulatory changes can be estimated using the module. 26 refs., 6 figs., 5 tabs.« less

Multivariate time series modeling of short-term system scale irrigation demand

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara

2015-12-01

Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as changing water policy, continued development of water markets, drought and changing technology.

An impact analysis of forecasting methods and forecasting parameters on bullwhip effect

NASA Astrophysics Data System (ADS)

Silitonga, R. Y. H.; Jelly, N.

2018-04-01

Bullwhip effect is an increase of variance of demand fluctuation from downstream to upstream of supply chain. Forecasting methods and forecasting parameters were recognized as some factors that affect bullwhip phenomena. To study these factors, we can develop simulations. There are several ways to simulate bullwhip effect in previous studies, such as mathematical equation modelling, information control modelling, computer program, and many more. In this study a spreadsheet program named Bullwhip Explorer was used to simulate bullwhip effect. Several scenarios were developed to show the change in bullwhip effect ratio because of the difference in forecasting methods and forecasting parameters. Forecasting methods used were mean demand, moving average, exponential smoothing, demand signalling, and minimum expected mean squared error. Forecasting parameters were moving average period, smoothing parameter, signalling factor, and safety stock factor. It showed that decreasing moving average period, increasing smoothing parameter, increasing signalling factor can create bigger bullwhip effect ratio. Meanwhile, safety stock factor had no impact to bullwhip effect.

Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

NASA Astrophysics Data System (ADS)

Sardinha-Lourenço, A.; Andrade-Campos, A.; Antunes, A.; Oliveira, M. S.

2018-03-01

Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.

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.

Forecasting paratransit services demand : review and recommendations.

DOT National Transportation Integrated Search

2013-06-01

Travel demand forecasting tools for Floridas paratransit services are outdated, utilizing old national trip : generation rate generalities and simple linear regression models. In its guidance for the development of : mandated Transportation Disadv...

An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models

An Optimization of Inventory Demand Forecasting in University Healthcare Centre

NASA Astrophysics Data System (ADS)

Bon, A. T.; Ng, T. K.

2017-01-01

Healthcare industry becomes an important field for human beings nowadays as it concerns about one’s health. With that, forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. Hence, a case study was conducted in University Health Centre to collect historical demand data of Panadol 650mg for 68 months from January 2009 until August 2014. The aim of the research is to optimize the overall inventory demand through forecasting techniques. Quantitative forecasting or time series forecasting model was used in the case study to forecast future data as a function of past data. Furthermore, the data pattern needs to be identified first before applying the forecasting techniques. Trend is the data pattern and then ten forecasting techniques are applied using Risk Simulator Software. Lastly, the best forecasting techniques will be find out with the least forecasting error. Among the ten forecasting techniques include single moving average, single exponential smoothing, double moving average, double exponential smoothing, regression, Holt-Winter’s additive, Seasonal additive, Holt-Winter’s multiplicative, seasonal multiplicative and Autoregressive Integrated Moving Average (ARIMA). According to the forecasting accuracy measurement, the best forecasting technique is regression analysis.

Sub-seasonal predictability of water scarcity at global and local scale

NASA Astrophysics Data System (ADS)

Wanders, N.; Wada, Y.; Wood, E. F.

2016-12-01

Forecasting the water demand and availability for agriculture and energy production has been neglected in previous research, partly due to the fact that most large-scale hydrological models lack the skill to forecast human water demands at sub-seasonal time scale. We study the potential of a sub-seasonal water scarcity forecasting system for improved water management decision making and improved estimates of water demand and availability. We have generated 32 years of global sub-seasonal multi-model water availability, demand and scarcity forecasts. The quality of the forecasts is compared to a reference forecast derived from resampling historic weather observations. The newly developed system has been evaluated for both the global scale and in a real-time local application in the Sacramento valley for the Trinity, Shasta and Oroville reservoirs, where the water demand for agriculture and hydropower is high. On the global scale we find that the reference forecast shows high initial forecast skill (up to 8 months) for water scarcity in the eastern US, Central Asia and Sub-Saharan Africa. Adding dynamical sub-seasonal forecasts results in a clear improvement for most regions in the world, increasing the forecasts' lead time by 2 or more months on average. The strongest improvements are found in the US, Brazil, Central Asia and Australia. For the Sacramento valley we can accurately predict anomalies in the reservoir inflow, hydropower potential and the downstream irrigation water demand 6 months in advance. This allow us to forecast potential water scarcity in the Sacramento valley and adjust the reservoir management to prevent deficits in energy or irrigation water availability. The newly developed forecast system shows that it is possible to reduce the vulnerability to upcoming water scarcity events and allows optimization of the distribution of the available water between the agricultural and energy sector half a year in advance.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department.

PubMed

Calegari, Rafael; Fogliatto, Flavio S; Lucini, Filipe R; Neyeloff, Jeruza; Kuchenbecker, Ricardo S; Schaan, Beatriz D

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department

PubMed Central

Fogliatto, Flavio S.; Neyeloff, Jeruza; Kuchenbecker, Ricardo S.; Schaan, Beatriz D.

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification. PMID:27725842

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

NASA Astrophysics Data System (ADS)

Wu, Qi

2010-03-01

Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

2016-06-01

Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

Demand forecasting for automotive sector in Malaysia by system dynamics approach

NASA Astrophysics Data System (ADS)

Zulkepli, Jafri; Fong, Chan Hwa; Abidin, Norhaslinda Zainal

2015-12-01

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

A Hybrid Approach on Tourism Demand Forecasting

NASA Astrophysics Data System (ADS)

Nor, M. E.; Nurul, A. I. M.; Rusiman, M. S.

2018-04-01

Tourism has become one of the important industries that contributes to the country’s economy. Tourism demand forecasting gives valuable information to policy makers, decision makers and organizations related to tourism industry in order to make crucial decision and planning. However, it is challenging to produce an accurate forecast since economic data such as the tourism data is affected by social, economic and environmental factors. In this study, an equally-weighted hybrid method, which is a combination of Box-Jenkins and Artificial Neural Networks, was applied to forecast Malaysia’s tourism demand. The forecasting performance was assessed by taking the each individual method as a benchmark. The results showed that this hybrid approach outperformed the other two models

Forecasting residential electricity demand in provincial China.

PubMed

Liao, Hua; Liu, Yanan; Gao, Yixuan; Hao, Yu; Ma, Xiao-Wei; Wang, Kan

2017-03-01

In China, more than 80% electricity comes from coal which dominates the CO2 emissions. Residential electricity demand forecasting plays a significant role in electricity infrastructure planning and energy policy designing, but it is challenging to make an accurate forecast for developing countries. This paper forecasts the provincial residential electricity consumption of China in the 13th Five-Year-Plan (2016-2020) period using panel data. To overcome the limitations of widely used predication models with unreliably prior knowledge on function forms, a robust piecewise linear model in reduced form is utilized to capture the non-deterministic relationship between income and residential electricity consumption. The forecast results suggest that the growth rates of developed provinces will slow down, while the less developed will be still in fast growing. The national residential electricity demand will increase at 6.6% annually during 2016-2020, and populous provinces such as Guangdong will be the main contributors to the increments.

Accuracy analysis of TDRSS demand forecasts

NASA Technical Reports Server (NTRS)

Stern, Daniel C.; Levine, Allen J.; Pitt, Karl J.

1994-01-01

This paper reviews Space Network (SN) demand forecasting experience over the past 16 years and describes methods used in the forecasts. The paper focuses on the Single Access (SA) service, the most sought-after resource in the Space Network. Of the ten years of actual demand data available, only the last five years (1989 to 1993) were considered predictive due to the extensive impact of the Challenger accident of 1986. NASA's Space Network provides tracking and communications services to user spacecraft such as the Shuttle and the Hubble Space Telescope. Forecasting the customer requirements is essential to planning network resources and to establishing service commitments to future customers. The lead time to procure Tracking and Data Relay Satellites (TDRS's) requires demand forecasts ten years in the future a planning horizon beyond the funding commitments for missions to be supported. The long range forecasts are shown to have had a bias toward underestimation in the 1991 -1992 period. The trend of underestimation can be expected to be replaced by overestimation for a number of years starting with 1998. At that time demand from new missions slated for launch will be larger than the demand from ongoing missions, making the potential for delay the dominant factor. If the new missions appear as scheduled, the forecasts are likely to be moderately underestimated. The SN commitment to meet the negotiated customer's requirements calls for conservatism in the forecasting. Modification of the forecasting procedure to account for a delay bias is, therefore, not advised. Fine tuning the mission model to more accurately reflect the current actual demand is recommended as it may marginally improve the first year forecasting.

Time series modelling and forecasting of emergency department overcrowding.

PubMed

Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Tahon, Christian

2014-09-01

Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

The Use of Artificial Neural Networks for Forecasting the Electric Demand of Stand-Alone Consumers

NASA Astrophysics Data System (ADS)

Ivanin, O. A.; Direktor, L. B.

2018-05-01

The problem of short-term forecasting of electric power demand of stand-alone consumers (small inhabited localities) situated outside centralized power supply areas is considered. The basic approaches to modeling the electric power demand depending on the forecasting time frame and the problems set, as well as the specific features of such modeling, are described. The advantages and disadvantages of the methods used for the short-term forecast of the electric demand are indicated, and difficulties involved in the solution of the problem are outlined. The basic principles of arranging artificial neural networks are set forth; it is also shown that the proposed method is preferable when the input information necessary for prediction is lacking or incomplete. The selection of the parameters that should be included into the list of the input data for modeling the electric power demand of residential areas using artificial neural networks is validated. The structure of a neural network is proposed for solving the problem of modeling the electric power demand of residential areas. The specific features of generation of the training dataset are outlined. The results of test modeling of daily electric demand curves for some settlements of Kamchatka and Yakutia based on known actual electric demand curves are provided. The reliability of the test modeling has been validated. A high value of the deviation of the modeled curve from the reference curve obtained in one of the four reference calculations is explained. The input data and the predicted power demand curves for the rural settlement of Kuokuiskii Nasleg are provided. The power demand curves were modeled for four characteristic days of the year, and they can be used in the future for designing a power supply system for the settlement. To enhance the accuracy of the method, a series of measures based on specific features of a neural network's functioning are proposed.

Developing a Universal Navy Uniform Adoption Model for Use in Forecasting

DTIC Science & Technology

2015-12-01

manpower , and allowance data in order to build the model. Once chosen, the best candidate model will be validated against alternate sales data from a...inventory shortage or excess inventory holding costs caused by overestimation. 14. SUBJECT TERMS demand management, demand forecasting, Defense...software will be used to identify relationships between uniform sales, time, manpower , and allowance data in order to build the model. Once chosen, the

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

PubMed

Wu, Hua'an; Zeng, Bo; Zhou, Meng

2017-11-15

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.

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 fluid milk and cheese demands for the next decade.

PubMed

Schmit, T M; Kaiser, H M

2006-12-01

Predictions of future market demands and farm prices for dairy products are important determinants in developing marketing strategies and farm-production planning decisions. The objective of this report was to use current aggregate forecast data, combined with existing econometric models of demand and supply, to forecast retail demands for fluid milk and cheese and the supply and price of farm milk over the next decade. In doing so, we can investigate whether projections of population and consumer food-spending patterns will extend or alter current consumption trends and examine the implications of future generic advertising strategies for dairy products. To conduct the forecast simulations and appropriately allocate the farm milk supply to various uses, we used a partial equilibrium model of the US domestic dairy sector that segmented the industry into retail, wholesale, and farm markets. Model simulation results indicated that declines in retail per capita demand would persist but at a reduced rate from years past and that retail per capita demand for cheese would continue to grow and strengthen over the next decade. These predictions rely on expected changes in the size of populations of various ages, races, and ethnicities and on existing patterns of spending on food at home and away from home. The combined effect of these forecasted changes in demand levels was reflected in annualized growth in the total farm-milk supply that was similar to growth realized during the past few years. Although we expect nominal farm milk prices to increase over the next decade, we expect real prices (relative to assumed growth in feed costs) to remain relatively stable and show no increase until the end of the forecast period. Supplemental industry model simulations also suggested that net losses in producer revenues would result if only nominal levels of generic advertising spending were maintained in forthcoming years. In fact, if real generic advertising expenditures are increased relative to 2005 levels, returns to the investment in generic advertising can be improved. Specifically, each additional real dollar invested in generic advertising for fluid milk and cheese products over the forecast period would result in an additional 5.61 dollars in producer revenues.

Demand forecasting for automotive sector in Malaysia by system dynamics approach

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

Zulkepli, Jafri, E-mail: zhjafri@uum.edu.my; Abidin, Norhaslinda Zainal, E-mail: nhaslinda@uum.edu.my; Fong, Chan Hwa, E-mail: hfchan7623@yahoo.com

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand frommore » the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.« less

Short-term energy outlook. Volume 2. Methodology

NASA Astrophysics Data System (ADS)

1983-05-01

Recent changes in forecasting methodology for nonutility distillate fuel oil demand and for the near-term petroleum forecasts are discussed. The accuracy of previous short-term forecasts of most of the major energy sources published in the last 13 issues of the Outlook is evaluated. Macroeconomic and weather assumptions are included in this evaluation. Energy forecasts for 1983 are compared. Structural change in US petroleum consumption, the use of appropriate weather data in energy demand modeling, and petroleum inventories, imports, and refinery runs are discussed.

A supply chain contract with flexibility as a risk-sharing mechanism for demand forecasting

NASA Astrophysics Data System (ADS)

Kim, Whan-Seon

2013-06-01

Demand forecasting is one of the main causes of the bullwhip effect in a supply chain. As a countermeasure for demand uncertainty as well as a risk-sharing mechanism for demand forecasting in a supply chain, this article studies a bilateral contract with order quantity flexibility. Under the contract, the buyer places orders in advance for the predetermined horizons and makes minimum purchase commitments. The supplier, in return, provides the buyer with the flexibility to adjust the order quantities later, according to the most updated demand information. To conduct comparative simulations, four-echelon supply chain models, that employ the contracts and different forecasting techniques under dynamic market demands, are developed. The simulation outcomes show that demand fluctuation can be effectively absorbed by the contract scheme, which enables better inventory management and customer service. Furthermore, it has been verified that the contract scheme under study plays a role as an effective coordination mechanism in a decentralised supply chain.

Forecasting the stochastic demand for inpatient care: the case of the Greek national health system.

PubMed

Boutsioli, Zoe

2010-08-01

The aim of this study is to estimate the unexpected demand of Greek public hospitals. A multivariate model with four explanatory variables is used. These are as follows: the weekend effect, the duty effect, the summer holiday and the official holiday. The method of the ordinary least squares is used to estimate the impact of these variables on the daily hospital emergency admissions series. The forecasted residuals of hospital regressions for each year give the estimated stochastic demand. Daily emergency admissions decline during weekends, summer months and official holidays, and increase on duty hospital days. Stochastic hospital demand varies both among hospitals and over the five-year time period under investigation. Variations among hospitals are larger than time variations. Hospital managers and health policy-makers can be availed by forecasting the future flows of emergent patients. The benefit can be both at managerial and economical level. More advanced models including additional daily variables such as the weather forecasts could provide more accurate estimations.

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.

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

Medium- and long-term electric power demand forecasting based on the big data of smart city

NASA Astrophysics Data System (ADS)

Wei, Zhanmeng; Li, Xiyuan; Li, Xizhong; Hu, Qinghe; Zhang, Haiyang; Cui, Pengjie

2017-08-01

Based on the smart city, this paper proposed a new electric power demand forecasting model, which integrates external data such as meteorological information, geographic information, population information, enterprise information and economic information into the big database, and uses an improved algorithm to analyse the electric power demand and provide decision support for decision makers. The data mining technology is used to synthesize kinds of information, and the information of electric power customers is analysed optimally. The scientific forecasting is made based on the trend of electricity demand, and a smart city in north-eastern China is taken as a sample.

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

PubMed

Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

2016-01-01

Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

Model documentation, Coal Market Module of the National Energy Modeling System

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

NONE

This report documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System`s (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1998 (AEO98). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS). CMM provides annual forecasts of prices, production, and consumption of coal for NEMS. In general, the CDS integrates the supply inputs from the CPS to satisfy demands for coal from exogenous demand models. The internationalmore » area of the CDS forecasts annual world coal trade flows from major supply to major demand regions and provides annual forecasts of US coal exports for input to NEMS. Specifically, the CDS receives minemouth prices produced by the CPS, demand and other exogenous inputs from other NEMS components, and provides delivered coal prices and quantities to the NEMS economic sectors and regions.« less

Nambe Pueblo Water Budget and Forecasting model.

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

Brainard, James Robert

2009-10-01

This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Watermore » Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.« less

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption

PubMed Central

Wu, Hua’an; Zhou, Meng

2017-01-01

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy. PMID:29140266

Travel demand modeling for the small and medium sized MPOs in Illinois.

DOT National Transportation Integrated Search

2011-09-01

Travel demand modeling is an important tool in the transportation planning community. It helps forecast travel : characteristics into the future at various planning levels such as state, region and corridor. Using travel demand : modeling to evaluate...

Distributed Generation Market Demand Model | NREL

Science.gov Websites

Demand Model The Distributed Generation Market Demand (dGen) model simulates the potential adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the dGen model can help develop deployment forecasts for distributed resources, including sensitivity to

Socioeconomic Forecasting : [Technical Summary

DOT National Transportation Integrated Search

2012-01-01

Because the traffic forecasts produced by the Indiana : Statewide Travel Demand Model (ISTDM) are driven by : the demographic and socioeconomic inputs to the model, : particular attention must be given to obtaining the most : accurate demographic and...

[Medical human resources planning in Europe: A literature review of the forecasting models].

PubMed

Benahmed, N; Deliège, D; De Wever, A; Pirson, M

2018-02-01

Healthcare is a labor-intensive sector in which half of the expenses are dedicated to human resources. Therefore, policy makers, at national and internal levels, attend to the number of practicing professionals and the skill mix. This paper aims to analyze the European forecasting model for supply and demand of physicians. To describe the forecasting tools used for physician planning in Europe, a grey literature search was done in the OECD, WHO, and European Union libraries. Electronic databases such as Pubmed, Medine, Embase and Econlit were also searched. Quantitative methods for forecasting medical supply rely mainly on stock-and-flow simulations and less often on systemic dynamics. Parameters included in forecasting models exhibit wide variability for data availability and quality. The forecasting of physician needs is limited to healthcare consumption and rarely considers overall needs and service targets. Besides quantitative methods, horizon scanning enables an evaluation of the changes in supply and demand in an uncertain future based on qualitative techniques such as semi-structured interviews, Delphi Panels, or focus groups. Finally, supply and demand forecasting models should be regularly updated. Moreover, post-hoc analyze is also needed but too rarely implemented. Medical human resource planning in Europe is inconsistent. Political implementation of the results of forecasting projections is essential to insure efficient planning. However, crucial elements such as mobility data between Member States are poorly understood, impairing medical supply regulation policies. These policies are commonly limited to training regulations, while horizontal and vertical substitution is less frequently taken into consideration. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Enhancing Nursing Staffing Forecasting With Safety Stock Over Lead Time Modeling.

PubMed

McNair, Douglas S

2015-01-01

In balancing competing priorities, it is essential that nursing staffing provide enough nurses to safely and effectively care for the patients. Mathematical models to predict optimal "safety stocks" have been routine in supply chain management for many years but have up to now not been applied in nursing workforce management. There are various aspects that exhibit similarities between the 2 disciplines, such as an evolving demand forecast according to acuity and the fact that provisioning "stock" to meet demand in a future period has nonzero variable lead time. Under assumptions about the forecasts (eg, the demand process is well fit as an autoregressive process) and about the labor supply process (≥1 shifts' lead time), we show that safety stock over lead time for such systems is effectively equivalent to the corresponding well-studied problem for systems with stationary demand bounds and base stock policies. Hence, we can apply existing models from supply chain analytics to find the optimal safety levels of nurse staffing. We use a case study with real data to demonstrate that there are significant benefits from the inclusion of the forecast process when determining the optimal safety stocks.

Capturing well-being in activity pattern models within activity-based travel demand models.

DOT National Transportation Integrated Search

2013-03-01

The activity-based approach which is based on the premise that the demand for travel is derived : from the demand for activities, currently constitutes the state of the art in metropolitan travel : demand forecasting and particularly in a form known ...

Capturing well-being in activity pattern models within activity-based travel demand models.

DOT National Transportation Integrated Search

2013-04-01

The activity-based approach which is based on the premise that the demand for travel is derived : from the demand for activities, currently constitutes the state of the art in metropolitan travel : demand forecasting and particularly in a form known ...

Forecast of the United States telecommunications demand through the year 2000

NASA Astrophysics Data System (ADS)

Kratochvil, D.

1984-01-01

The telecommunications forecasts considered in the present investigation were developed in studies conducted by Kratochvil et al. (1983). The overall purpose of these studies was to forecast the potential U.S. domestic telecommunications demand for satellite-provided fixed communications voice, data, and video services through the year 2000, so that this information on service demand would be available to aid in NASA communications program planning. Aspects of forecasting methodology are discussed, taking into account forecasting activity flow, specific services and selected techniques, and an event/trend cross-impact model. Events, or market determinant factors, which are very likely to occur by 1995 and 2005, are presented in a table. It is found that the demand for telecommunications in general, and for satellite telecommunications in particular, will increase significantly between now and the year 2000. The required satellite capacity will surpass both the potential and actual capacities in the early 1990s, indicating a need for Ka-band at that time.

Using Seasonal Forecasts for medium-term Electricity Demand Forecasting on Italy

NASA Astrophysics Data System (ADS)

De Felice, M.; Alessandri, A.; Ruti, P.

2012-12-01

Electricity demand forecast is an essential tool for energy management and operation scheduling for electric utilities. In power engineering, medium-term forecasting is defined as the prediction up to 12 months ahead, and commonly is performed considering weather climatology and not actual forecasts. This work aims to analyze the predictability of electricity demand on seasonal time scale, considering seasonal samples, i.e. average on three months. Electricity demand data has been provided by Italian Transmission System Operator for eight different geographical areas, in Fig. 1 for each area is shown the average yearly demand anomaly for each season. This work uses data for each summer during 1990-2010 and all the datasets have been pre-processed to remove trends and reduce the influence of calendar and economic effects. The choice of focusing this research on the summer period is due to the critical peaks of demand that power grid is subject during hot days. Weather data have been included considering observations provided by ECMWF ERA-INTERIM reanalyses. Primitive variables (2-metres temperature, pressure, etc) and derived variables (cooling and heating degree days) have been averaged for summer months. A particular attention has been given to the influence of persistence of positive temperature anomaly and a derived variable which count the number of consecutive days of extreme-days has been used. Electricity demand forecast has been performed using linear and nonlinear regression methods and stepwise model selection procedures have been used to perform a variable selection with respect to performance measures. Significance tests on multiple linear regression showed the importance of cooling degree days during summer in the North-East and South of Italy with an increase of statistical significance after 2003, a result consistent with the diffusion of air condition and ventilation equipment in the last decade. Finally, using seasonal climate forecasts we evaluate the performances of electricity demand forecast performed with predicted variables on Italian regions with encouraging results on the South of Italy. This work gives an initial assessment on the predictability of electricity demand on seasonal time scale, evaluating the relevance of climate information provided by seasonal forecasts for electricity management during high-demand periods.;

Satellite provided customer premise services: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution, potential CPS (customers premises services) user classes, net long haul forecasts, CPS cost analysis, overall satellite forecast, CPS satellite market, Ka-band CPS satellite forecast, nationwide traffic distribution model, and intra-urban topology are discussed.

Satellite provided customer premise services: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Astrophysics Data System (ADS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-08-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution, potential CPS (customers premises services) user classes, net long haul forecasts, CPS cost analysis, overall satellite forecast, CPS satellite market, Ka-band CPS satellite forecast, nationwide traffic distribution model, and intra-urban topology are discussed.

Evaluation of Ensemble Water Supply and Demands Forecasts for Water Management in the Klamath River Basin

NASA Astrophysics Data System (ADS)

Broman, D.; Gangopadhyay, S.; McGuire, M.; Wood, A.; Leady, Z.; Tansey, M. K.; Nelson, K.; Dahm, K.

2017-12-01

The Upper Klamath River Basin in south central Oregon and north central California is home to the Klamath Irrigation Project, which is operated by the Bureau of Reclamation and provides water to around 200,000 acres of agricultural lands. The project is managed in consideration of not only water deliveries to irrigators, but also wildlife refuge water demands, biological opinion requirements for Endangered Species Act (ESA) listed fish, and Tribal Trust responsibilities. Climate change has the potential to impact water management in terms of volume and timing of water and the ability to meet multiple objectives. Current operations use a spreadsheet-based decision support tool, with water supply forecasts from the National Resources Conservation Service (NRCS) and California-Nevada River Forecast Center (CNRFC). This tool is currently limited in its ability to incorporate in ensemble forecasts, which offer the potential for improved operations by quantifying forecast uncertainty. To address these limitations, this study has worked to develop a RiverWare based water resource systems model, flexible enough to use across multiple decision time-scales, from short-term operations out to long-range planning. Systems model development has been accompanied by operational system development to handle data management and multiple modeling components. Using a set of ensemble hindcasts, this study seeks to answer several questions: A) Do a new set of ensemble streamflow forecasts have additional skill beyond what?, and allow for improved decision making under changing conditions? B) Do net irrigation water requirement forecasts developed in this project to quantify agricultural demands and reservoir evaporation forecasts provide additional benefits to decision making beyond water supply forecasts? C) What benefit do ensemble forecasts have in the context of water management decisions?

Dynamics of electricity market correlations

NASA Astrophysics Data System (ADS)

Alvarez-Ramirez, J.; Escarela-Perez, R.; Espinosa-Perez, G.; Urrea, R.

2009-06-01

Electricity market participants rely on demand and price forecasts to decide their bidding strategies, allocate assets, negotiate bilateral contracts, hedge risks, and plan facility investments. However, forecasting is hampered by the non-linear and stochastic nature of price time series. Diverse modeling strategies, from neural networks to traditional transfer functions, have been explored. These approaches are based on the assumption that price series contain correlations that can be exploited for model-based prediction purposes. While many works have been devoted to the demand and price modeling, a limited number of reports on the nature and dynamics of electricity market correlations are available. This paper uses detrended fluctuation analysis to study correlations in the demand and price time series and takes the Australian market as a case study. The results show the existence of correlations in both demand and prices over three orders of magnitude in time ranging from hours to months. However, the Hurst exponent is not constant over time, and its time evolution was computed over a subsample moving window of 250 observations. The computations, also made for two Canadian markets, show that the correlations present important fluctuations over a seasonal one-year cycle. Interestingly, non-linearities (measured in terms of a multifractality index) and reduced price predictability are found for the June-July periods, while the converse behavior is displayed during the December-January period. In terms of forecasting models, our results suggest that non-linear recursive models should be considered for accurate day-ahead price estimation. On the other hand, linear models seem to suffice for demand forecasting purposes.

Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia

PubMed Central

Strager, Michael P.; Strager, Jacquelyn M.; Evans, Jeffrey S.; Dunscomb, Judy K.; Kreps, Brad J.; Maxwell, Aaron E.

2015-01-01

Predicting the locations of future surface coal mining in Appalachia is challenging for a number of reasons. Economic and regulatory factors impact the coal mining industry and forecasts of future coal production do not specifically predict changes in location of future coal production. With the potential environmental impacts from surface coal mining, prediction of the location of future activity would be valuable to decision makers. The goal of this study was to provide a method for predicting future surface coal mining extents under changing economic and regulatory forecasts through the year 2035. This was accomplished by integrating a spatial model with production demand forecasts to predict (1 km2) gridded cell size land cover change. Combining these two inputs was possible with a ratio which linked coal extraction quantities to a unit area extent. The result was a spatial distribution of probabilities allocated over forecasted demand for the Appalachian region including northern, central, southern, and eastern Illinois coal regions. The results can be used to better plan for land use alterations and potential cumulative impacts. PMID:26090883

Motor Vehicle Demand Models : Assessment of the State of the Art and Directions for Future Research

DOT National Transportation Integrated Search

1981-04-01

The report provides an assessment of the current state of motor vehicle demand modeling. It includes a detailed evaluation of one leading large-scale econometric vehicle demand model, which is tested for both logical consistency and forecasting accur...

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.

Stated choice for transportation demand management models : using a disaggregate truth set to study predictive validity

DOT National Transportation Integrated Search

1997-01-01

Discrete choice models have expanded the ability of transportation planners to forecast future trends. Where new services or policies are proposed, the stated-choice approach can provide an objective basis for forecasts. Stated-choice models are subj...

Projected electric power demands for the Potomac Electric Power Company

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

Wilson, J.W.

1975-07-01

Included are chapters on the background of the Potomac Electric Power Company, forecasting future power demand, demand modeling, accuracy of market predictions, and total power system requirements. (DG)

How is the weather? Forecasting inpatient glycemic control

PubMed Central

Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

2017-01-01

Aim: Apply methods of damped trend analysis to forecast inpatient glycemic control. Method: Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. Results: The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Conclusion: Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement. PMID:29134125

Florida Model Information eXchange System (MIXS).

DOT National Transportation Integrated Search

2013-08-01

Transportation planning largely relies on travel demand forecasting, which estimates the number and type of vehicles that will use a roadway at some point in the future. Forecasting estimates are made by computer models that use a wide variety of dat...

Economic Models for Projecting Industrial Capacity for Defense Production: A Review

DTIC Science & Technology

1983-02-01

macroeconomic forecast to establish the level of civilian final demand; all use the DoD Bridge Table to allocate budget category outlays to industries. Civilian...output table.’ 3. Macroeconomic Assumptions and the Prediction of Final Demand All input-output models require as a starting point a prediction of final... macroeconomic fore- cast of GNP and its components and (2) a methodology to transform these forecast values of consumption, investment, exports, etc. into

Development of weekend travel demand and mode choice models : final report, June 2009.

DOT National Transportation Integrated Search

2010-06-30

Travel demand models are widely used for forecasting and analyzing policies for automobile and transit travel. However, these models are typically developed for average weekday travel when regular activities are routine. The weekday models focus prim...

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.

A retrospective evaluation of traffic forecasting techniques.

DOT National Transportation Integrated Search

2016-08-01

Traffic forecasting techniquessuch as extrapolation of previous years traffic volumes, regional travel demand models, or : local trip generation rateshelp planners determine needed transportation improvements. Thus, knowing the accuracy of t...

Evaluation of origin-destination matrix estimation techniques to support aspects of traffic modeling.

DOT National Transportation Integrated Search

2014-05-01

Travel demand forecasting models are used to predict future traffic volumes to evaluate : roadway improvement alternatives. Each of the metropolitan planning organizations (MPO) in : Alabama maintains a travel demand model to support planning efforts...

A scoping review of nursing workforce planning and forecasting research.

PubMed

Squires, Allison; Jylhä, Virpi; Jun, Jin; Ensio, Anneli; Kinnunen, Juha

2017-11-01

This study will critically evaluate forecasting models and their content in workforce planning policies for nursing professionals and to highlight the strengths and the weaknesses of existing approaches. Although macro-level nursing workforce issues may not be the first thing that many nurse managers consider in daily operations, the current and impending nursing shortage in many countries makes nursing specific models for workforce forecasting important. A scoping review was conducted using a directed and summative content analysis approach to capture supply and demand analytic methods of nurse workforce planning and forecasting. The literature on nurse workforce forecasting studies published in peer-reviewed journals as well as in grey literature was included in the scoping review. Thirty six studies met the inclusion criteria, with the majority coming from the USA. Forecasting methods were biased towards service utilization analyses and were not consistent across studies. Current methods for nurse workforce forecasting are inconsistent and have not accounted sufficiently for socioeconomic and political factors that can influence workforce projections. Additional studies examining past trends are needed to improve future modelling. Accurate nursing workforce forecasting can help nurse managers, administrators and policy makers to understand the supply and demand of the workforce to prepare and maintain an adequate and competent current and future workforce. © 2017 John Wiley & Sons Ltd.

Considering inventory distributions in a stochastic periodic inventory routing system

NASA Astrophysics Data System (ADS)

Yadollahi, Ehsan; Aghezzaf, El-Houssaine

2017-07-01

Dealing with the stochasticity of parameters is one of the critical issues in business and industry nowadays. Supply chain planners have difficulties in forecasting stochastic parameters of a distribution system. Demand rates of customers during their lead time are one of these parameters. In addition, holding a huge level of inventory at the retailers is costly and inefficient. To cover the uncertainty of forecasting demand rates, researchers have proposed the usage of safety stock to avoid stock-out. However, finding the precise level of safety stock depends on forecasting the statistical distribution of demand rates and their variations in different settings among the planning horizon. In this paper the demand rate distributions and its parameters are taken into account for each time period in a stochastic periodic IRP. An analysis of the achieved statistical distribution of the inventory and safety stock level is provided to measure the effects of input parameters on the output indicators. Different values for coefficient of variation are applied to the customers' demand rate in the optimization model. The outcome of the deterministic equivalent model of SPIRP is simulated in form of an illustrative case.

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.

Energy demand forecasting

NASA Astrophysics Data System (ADS)

Energy demand forecasting and its connection with national energy policies and decisions is examined in light of recent, sharply revised estimates of future energy requirements. Techniques of economic projects are examined. Modeling of energy demands is discussed. Renewable energy sources are discussed. The shift away from reliance of domestic users on oil and natural gas toward electricity as a primary energy resource is examined in the context of the need to conserve energy and expand generating capacity in order to avoid a significant electricity shortfall.

Medium-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bao, Fangmin; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Mao, Yubin; Wang, Jiangbo; Liu, Junhui

2018-06-01

Electric demand forecasting is a basic work to ensure the safe operation of power system. Based on the theories of experimental economics and econometrics, this paper introduces Prognoz Platform 7.2 intelligent adaptive modeling platform, and constructs the economic electricity transmission model that considers the economic development scenarios and the dynamic adjustment of industrial structure to predict the region's annual electricity demand, and the accurate prediction of the whole society's electricity consumption is realized. Firstly, based on the theories of experimental economics and econometrics, this dissertation attempts to find the economic indicator variables that drive the most economical growth of electricity consumption and availability, and build an annual regional macroeconomic forecast model that takes into account the dynamic adjustment of industrial structure. Secondly, it innovatively put forward the economic electricity directed conduction theory and constructed the economic power transfer function to realize the group forecast of the primary industry + rural residents living electricity consumption, urban residents living electricity, the second industry electricity consumption, the tertiary industry electricity consumption; By comparing with the actual value of economy and electricity in Henan province in 2016, the validity of EETM model is proved, and the electricity consumption of the whole province from 2017 to 2018 is predicted finally.

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

TRANPLAN and GIS support for agencies in Alabama

DOT National Transportation Integrated Search

2001-08-06

Travel demand models are computerized programs intended to forecast future roadway traffic volumes for a community based on selected socioeconomic variables and travel behavior algorithms. Software to operate these travel demand models is currently a...

Short-term energy outlook, Annual supplement 1995

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

NONE

1995-07-25

This supplement is published once a year as a complement to the Short- Term Energy Outlook, Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts. Chap. 2 analyzes the response of the US petroleum industry to the recent four Federal environmental rules on motor gasoline. Chap. 3 compares the EIA base or mid case energy projections for 1995 and 1996 (as published in the first quarter 1995 Outlook) with recent projections made by fourmore » other major forecasting groups. Chap. 4 evaluates the overall accuracy. Chap. 5 presents the methology used in the Short- Term Integrated Forecasting Model for oxygenate supply/demand balances. Chap. 6 reports theoretical and empirical results from a study of non-transportation energy demand by sector. The empirical analysis involves the short-run energy demand in the residential, commercial, industrial, and electrical utility sectors in US.« less

Research on water shortage risks and countermeasures in North China

NASA Astrophysics Data System (ADS)

Cheng, Yuxiang; Fang, Wenxuan; Wu, Ziqin

2017-05-01

In the paper, a grey forecasting model and a population growth model are established for forecasting water resources supply and demand situation in the region, and evaluating the scarcity of water resources thereof in order to solve the problem of water shortage in North China. A concrete plan for alleviating water resources pressure is proposed with AHP as basis, thereby discussing the feasibility of the plan. Firstly, water resources supply and demand in the future 15 years are predicted. There are four sources for the demand of water resources mainly: industry, agriculture, ecology and resident living. Main supply sources include surface water and underground water resources. A grey forecasting method is adopted for predicting in the paper aiming at water resources demands since industrial, agricultural and ecological water consumption data have excessive decision factors and the correlation is relatively fuzzy. Since residents' water consumption is determined by per capita water consumption and local population, a logistic growth model is adopted to forecast the population. The grey forecasting method is used for predicting per capita water consumption, and total water demand can be obtained finally. International calculation standards are adopted as reference aiming at water supply. The grey forecasting method is adopted for forecasting surface water quantity and underground water quantity, and water resources supply is obtained finally. Per capita water availability in the region is calculated by comparing the water resources supply and demand. Results show that per capita water availability in the region is only 283 cubic meters this year, people live in serious water shortage region, who will suffer from water shortage state for long time. Then, sensitivity analysis is applied for model test. The test result is excellent, and the prediction results are more accurate. In the paper, the following measures are proposed for improving water resources condition in the region according to prediction results, such as construction of reservoirs, sewage treatment, water diversion project and other measures. A detailed water supply plan is formulated. Water supply weights of all measures are determined according to the AHP model. Solution is sought after original models are improved. Results show that water resources quantity per capita will be up to 2170 cubic meters or so this year, people suffer from moderate water shortage in the region, which can meet people's life needs and economic development needs basically. In addition, water resources quantity per capita is increased year by year, and it can reach mild water shortage level after 2030. In a word, local water resources dilemma can be effectively solved by the plan actually, and thoughts can be provided for decision makers.

Disaggregating residential water demand for improved forecasts and decision making

NASA Astrophysics Data System (ADS)

Woodard, G.; Brookshire, D.; Chermak, J.; Krause, K.; Roach, J.; Stewart, S.; Tidwell, V.

2003-04-01

Residential water demand is the product of population and per capita demand. Estimates of per capita demand often are based on econometric models of demand, usually based on time series data of demand aggregated at the water provider level. Various studies have examined the impact of such factors as water pricing, weather, and income, with many other factors and details of water demand remaining unclear. Impacts of water conservation programs often are estimated using simplistic engineering calculations. Partly as a result of this, policy discussions regarding water demand management often focus on water pricing, water conservation, and growth control. Projecting water demand is often a straight-forward, if fairly uncertain process of forecasting population and per capita demand rates. SAHRA researchers are developing improved forecasts of residential water demand by disaggregating demand to the level of individuals, households, and specific water uses. Research results based on high-resolution water meter loggers, household-level surveys, economic experiments and recent census data suggest that changes in wealth, household composition, and individual behavior may affect demand more than changes in population or the stock of landscape plants, water-using appliances and fixtures, generally considered the primary determinants of demand. Aging populations and lower fertility rates are dramatically reducing household size, thereby increasing the number of households and residences for a given population. Recent prosperity and low interest rates have raised home ownership rates to unprecented levels. These two trends are leading to increased per capita outdoor water demand. Conservation programs have succeeded in certain areas, such as promoting drought-tolerant native landscaping, but have failed in other areas, such as increasing irrigation efficiency or curbing swimming pool water usage. Individual behavior often is more important than the household's stock of water-using fixtures, and ranges from hedonism (installing pools and whirlpool tubs) to satisficing (adjusting irrigation timers only twice per year) to acting on deeply-held conservation ethics in ways that not only fail any benefit-cost test, but are discouraged, or even illegal (reuse of gray water and black water). Research findings are being captured in dynamic simulation models that integrate social and natural science to create tools to assist water resource managers in providing sustainable water supplies and improving residential water demand forecasts. These models feature simple, graphical user interfaces and output screens that provide decision makers with visual, easy-to-understand information at the basin level. The models reveal connections between various supply and demand components, and highlight direct impacts and feedback mechanisms associated with various policy options.

Socioeconomic Forecasting Model for the Tri-County Regional Planning Commission

DOT National Transportation Integrated Search

1997-01-01

Socioeconomic data is a critical input to transportation planning and travel demand forecasting. Accurate estimates of existing population, incomes, employment and other socioeconomic characteristics are necessary for meaningful calibration of a trav...

Improving socioeconomic land use forecasting for medium-sized metropolitan organizations in Virginia.

DOT National Transportation Integrated Search

2008-01-01

Socioeconomic forecasts are the foundation for long range travel demand modeling, projecting variables such as population, households, employment, and vehicle ownership. In Virginia, metropolitan planning organizations (MPOs) develop socioeconomic fo...

Worldwide transportation/energy demand, 1975-2000. Revised Variflex model projections

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

Ayres, R.U.; Ayres, L.W.

1980-03-01

The salient features of the transportation-energy relationships that characterize the world of 1975 are reviewed, and worldwide (34 countries) long-range transportation demand by mode to the year 2000 is reviewed. A worldwide model is used to estimate future energy demand for transportation. Projections made by the forecasting model indicate that in the year 2000, every region will be more dependent on petroleum for the transportation sector than it was in 1975. This report is intended to highlight certain trends and to suggest areas for further investigation. Forecast methodology and model output are described in detail in the appendices. The reportmore » is one of a series addressing transportation energy consumption; it supplants and replaces an earlier version published in October 1978 (ORNL/Sub-78/13536/1).« less

Forecasting daily patient volumes in the emergency department.

PubMed

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

Commercial Demand Module - NEMS Documentation

EIA Publications

2017-01-01

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

Forecast of the World's Electrical Demands until 2025.

ERIC Educational Resources Information Center

Claverie, Maurice J.; Dupas, Alain P.

1979-01-01

Models of global energy demand, a lower-growth-rate model developed at Case Western Reserve University and the H5 model of the Conservation Committee of the World Energy Conference, assess the features of decentralized and centralized electricity generation in the years 2000 and 2025. (BT)

Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.

Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Astrophysics Data System (ADS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-09-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.

Relation of land use/land cover to resource demands

NASA Technical Reports Server (NTRS)

Clayton, C.

1981-01-01

Predictive models for forecasting residential energy demand are investigated. The models are examined in the context of implementation through manipulation of geographic information systems containing land use/cover information. Remotely sensed data is examined as a possible component in this process.

Using Sensor Web Processes and Protocols to Assimilate Satellite Data into a Forecast Model

NASA Technical Reports Server (NTRS)

Goodman, H. Michael; Conover, Helen; Zavodsky, Bradley; Maskey, Manil; Jedlovec, Gary; Regner, Kathryn; Li, Xiang; Lu, Jessica; Botts, Mike; Berthiau, Gregoire

2008-01-01

The goal of the Sensor Management Applied Research Technologies (SMART) On-Demand Modeling project is to develop and demonstrate the readiness of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities to integrate both space-based Earth observations and forecast model output into new data acquisition and assimilation strategies. The project is developing sensor web-enabled processing plans to assimilate Atmospheric Infrared Sounding (AIRS) satellite temperature and moisture retrievals into a regional Weather Research and Forecast (WRF) model over the southeastern United States.

A System Dynamics Modeling of Water Supply and Demand in Las Vegas Valley

NASA Astrophysics Data System (ADS)

Parajuli, R.; Kalra, A.; Mastino, L.; Velotta, M.; Ahmad, S.

2017-12-01

The rise in population and change in climate have posed the uncertainties in the balance between supply and demand of water. The current study deals with the water management issues in Las Vegas Valley (LVV) using Stella, a system dynamics modeling software, to model the feedback based relationship between supply and demand parameters. Population parameters were obtained from Center for Business and Economic Research while historical water demand and conservation practices were modeled as per the information provided by local authorities. The water surface elevation of Lake Mead, which is the prime source of water supply to the region, was modeled as the supply side whereas the water demand in LVV was modeled as the demand side. The study was done from the period of 1989 to 2049 with 1989 to 2012 as the historical one and the period from 2013 to 2049 as the future period. This study utilizes Coupled Model Intercomparison Project data sets (2013-2049) (CMIP3&5) to model different future climatic scenarios. The model simulates the past dynamics of supply and demand, and then forecasts the future water budget for the forecasted future population and future climatic conditions. The results can be utilized by the water authorities in understanding the future water status and hence plan suitable conservation policies to allocate future water budget and achieve sustainable water management.

Techniques for water demand analysis and forecasting: Puerto Rico, a case study

USGS Publications Warehouse

Attanasi, E.D.; Close, E.R.; Lopez, M.A.

1975-01-01

The rapid economic growth of the Commonwealth-of Puerto Rico since 1947 has brought public pressure on Government agencies for rapid development of public water supply and waste treatment facilities. Since 1945 the Puerto Rico Aqueduct and Sewer Authority has had the responsibility for planning, developing and operating water supply and waste treatment facilities on a municipal basis. The purpose of this study was to develop operational techniques whereby a planning agency, such as the Puerto Rico Aqueduct and Sewer Authority, could project the temporal and spatial distribution of .future water demands. This report is part of a 2-year cooperative study between the U.S. Geological Survey and the Environmental Quality Board of the Commonwealth of Puerto Rico, for the development of systems analysis techniques for use in water resources planning. While the Commonwealth was assisted in the development of techniques to facilitate ongoing planning, the U.S. Geological Survey attempted to gain insights in order to better interface its data collection efforts with the planning process. The report reviews the institutional structure associated with water resources planning for the Commonwealth. A brief description of alternative water demand forecasting procedures is presented and specific techniques and analyses of Puerto Rico demand data are discussed. Water demand models for a specific area of Puerto Rico are then developed. These models provide a framework for making several sets of water demand forecasts based on alternative economic and demographic assumptions. In the second part of this report, the historical impact of water resources investment on regional economic development is analyzed and related to water demand .forecasting. Conclusions and future data needs are in the last section.

A novel hybrid ensemble learning paradigm for tourism forecasting

NASA Astrophysics Data System (ADS)

Shabri, Ani

2015-02-01

In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

Influence of Forecast Accuracy of Photovoltaic Power Output on Capacity Optimization of Microgrid Composition under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Sone, Akihito; Kato, Takeyoshi; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). If a number of MGs are controlled to maintain the predetermined electricity demand including RE-based DGs as negative demand, they would contribute to supply-demand balancing of whole electric power system. For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on a demonstrative study on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization. Three forecast cases with different accuracy are compared. The main results are as follows. Even with no forecast error during every 30 min. as the ideal forecast method, the required capacity of NaS battery reaches about 40% of PVS capacity for mitigating the instantaneous forecast error within 30 min. The required capacity to compensate for the forecast error is doubled with the actual forecast method. The influence of forecast error can be reduced by adjusting the scheduled power output of controllable DGs according to the weather forecast. Besides, the required capacity can be reduced significantly if the error of balancing control in a MG is acceptable for a few percentages of periods, because the total periods of large forecast error is not so often.

New Approaches to Travel Forecasting Models: A Synthesis of Four Research Proposals

DOT National Transportation Integrated Search

1994-01-01

In July 1992, the Federal Highway Administration (FHWA) issued a solicitation for proposals to redesign the travel demand forecasting process. The purpose of the solicitation was to enable travel behavior researchers to explain how transportation pla...

Prediction of a service demand using combined forecasting approach

NASA Astrophysics Data System (ADS)

Zhou, Ling

2017-08-01

Forecasting facilitates cutting down operational and management costs while ensuring service level for a logistics service provider. Our case study here is to investigate how to forecast short-term logistic demand for a LTL carrier. Combined approach depends on several forecasting methods simultaneously, instead of a single method. It can offset the weakness of a forecasting method with the strength of another, which could improve the precision performance of prediction. Main issues of combined forecast modeling are how to select methods for combination, and how to find out weight coefficients among methods. The principles of method selection include that each method should apply to the problem of forecasting itself, also methods should differ in categorical feature as much as possible. Based on these principles, exponential smoothing, ARIMA and Neural Network are chosen to form the combined approach. Besides, least square technique is employed to settle the optimal weight coefficients among forecasting methods. Simulation results show the advantage of combined approach over the three single methods. The work done in the paper helps manager to select prediction method in practice.

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.

Sales forecasting newspaper with ARIMA: A case study

NASA Astrophysics Data System (ADS)

Permatasari, Carina Intan; Sutopo, Wahyudi; Hisjam, Muh.

2018-02-01

People are beginning to switch to using digital media for their daily activities, including changes in newspaper reading patterns to electronic news. In uncertainty trend, the customers of printed newspaper also have switched to electronic news. It has some negative effects on the printed newspaper demand, where there is often an inaccuracy of supply with demand which means that many newspapers are returned. The aim of this paper is to predict printed newspaper demand as accurately as possible to minimize the number of returns, to keep off the missed sales and to restrain the oversupply. The autoregressive integrated moving average (ARIMA) models were adopted to predict the right number of newspapers for a real case study of a newspaper company in Surakarta. The model parameters were found using maximum likelihood method. Then, the software Eviews 9 were utilized to forecasting any particular variables in the newspaper industry. This paper finally presents the appropriate of modeling and sales forecasting newspaper based on the output of the ARIMA models. In particular, it can be recommended to use ARIMA (1, 1, 0) model in predicting the number of newspapers. ARIMA (1, 1, 0) model was chosen from three different models that it provides the smallest value of the mean absolute percentage error (MAPE).

National Freight Demand Modeling - Bridging the Gap between Freight Flow Statistics and U.S. Economic Patterns

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

Chin, Shih-Miao; Hwang, Ho-Ling

2007-01-01

This paper describes a development of national freight demand models for 27 industry sectors covered by the 2002 Commodity Flow Survey. It postulates that the national freight demands are consistent with U.S. business patterns. Furthermore, the study hypothesizes that the flow of goods, which make up the national production processes of industries, is coherent with the information described in the 2002 Annual Input-Output Accounts developed by the Bureau of Economic Analysis. The model estimation framework hinges largely on the assumption that a relatively simple relationship exists between freight production/consumption and business patterns for each industry defined by the three-digit Northmore » American Industry Classification System industry codes (NAICS). The national freight demand model for each selected industry sector consists of two models; a freight generation model and a freight attraction model. Thus, a total of 54 simple regression models were estimated under this study. Preliminary results indicated promising freight generation and freight attraction models. Among all models, only four of them had a R2 value lower than 0.70. With additional modeling efforts, these freight demand models could be enhanced to allow transportation analysts to assess regional economic impacts associated with temporary lost of transportation services on U.S. transportation network infrastructures. Using such freight demand models and available U.S. business forecasts, future national freight demands could be forecasted within certain degrees of accuracy. These freight demand models could also enable transportation analysts to further disaggregate the CFS state-level origin-destination tables to county or zip code level.« less

Multiple Model Demand Forecasting Compared to Air Force Logistics Command D062 Performance.

DTIC Science & Technology

1980-06-01

SRI" 1002. 930. 53.4. 46 . 074 . 119. 01. 93. 249. 224.4 METHOD $1L 4 1 2 6 4 3 2 5 FOCUS FOIC 860. 262 . 049. 9133. 966. 931. 6?. 498. 662. 21.2 -13.9...5001. 5124. 426. 1192.0 150.0 SMITH II 4018. 4640 . 3620. 9650. 9587. 8582. 965. 3937. 2468. 2486.7 2363.7 TREND 3786. 3842. 1584. 2760. 4518. 4638...FORECASTS BASD( UPON IDENTICAL DEMND DATA TEM I 30 QUARTER 2 3 4 5 6 7 0 9 RAN DIAS ACT DEMAND 262 . 269. 265. 250. 259. 262 . 265. 265. 262 . FORECAST

Projected electric power demands for the Potomac Electric Power Company. Volume 1

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

Estomin, S.; Kahal, M.

1984-03-01

This three-volume report presents the results of an econometric forecast of peak and electric power demands for the Potomac Electric Power Company (PEPCO) through the year 2002. Volume I describes the methodology, the results of the econometric estimations, the forecast assumptions and the calculated forecasts of peak demand and energy usage. Separate sets of models were developed for the Maryland Suburbs (Montgomery and Prince George's counties), the District of Columbia and Southern Maryland (served by a wholesale customer of PEPCO). For each of the three jurisdictions, energy equations were estimated for residential and commercial/industrial customers for both summer and wintermore » seasons. For the District of Columbia, summer and winter equations for energy sales to the federal government were also estimated. Equations were also estimated for street lighting and energy losses. Noneconometric techniques were employed to forecast energy sales to the Northern Virginia suburbs, Metrorail and federal government facilities located in Maryland.« less

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 1. The Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Microsimulation of household and firm behaviors : coupled models of land use and travel demand in Austin, Texas

DOT National Transportation Integrated Search

2007-12-01

Households and firms are key drivers of urban growth, yet models for forecasting travel demand often : ignore their dynamic evolution and several key decision processes. An understanding of household and : firm behavior over time is critical in antic...

Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile

NASA Astrophysics Data System (ADS)

Delorit, Justin; Cristian Gonzalez Ortuya, Edmundo; Block, Paul

2017-09-01

In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October-January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950-2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The methods applied here advance the understanding of the mechanisms and timing responsible for moisture transport to the Elqui Valley and provide a unique application of streamflow forecasting in the prediction of water right allocations.

Diversity modelling for electrical power system simulation

NASA Astrophysics Data System (ADS)

Sharip, R. M.; Abu Zarim, M. A. U. A.

2013-12-01

This paper considers diversity of generation and demand profiles against the different future energy scenarios and evaluates these on a technical basis. Compared to previous studies, this research applied a forecasting concept based on possible growth rates from publically electrical distribution scenarios concerning the UK. These scenarios were created by different bodies considering aspects such as environment, policy, regulation, economic and technical. In line with these scenarios, forecasting is on a long term timescale (up to every ten years from 2020 until 2050) in order to create a possible output of generation mix and demand profiles to be used as an appropriate boundary condition for the network simulation. The network considered is a segment of rural LV populated with a mixture of different housing types. The profiles for the 'future' energy and demand have been successfully modelled by applying a forecasting method. The network results under these profiles shows for the cases studied that even though the value of the power produced from each Micro-generation is often in line with the demand requirements of an individual dwelling there will be no problems arising from high penetration of Micro-generation and demand side management for each dwellings considered. The results obtained highlight the technical issues/changes for energy delivery and management to rural customers under the future energy scenarios.

Analysis of Numerical Weather Predictions of Reference Evapotranspiration and Precipitation

NASA Astrophysics Data System (ADS)

Bughici, Theodor; Lazarovitch, Naftali; Fredj, Erick; Tas, Eran

2017-04-01

This study attempts to improve the forecast skill of the evapotranspiration (ET0) and Precipitation for the purpose of crop irrigation management over Israel using the Weather Research and Forecasting (WRF) Model. Optimized crop irrigation, in term of timing and quantities, decreases water and agrochemicals demand. Crop water demands depend on evapotranspiration and precipitation. The common method for computing reference evapotranspiration, for agricultural needs, ET0, is according to the FAO Penman-Monteith equation. The weather variables required for ET0 calculation (air temperature, relative humidity, wind speed and solar irradiance) are estimated by the WRF model. The WRF Model with two-way interacting domains at horizontal resolutions of 27, 9 and 3 km is used in the study. The model prediction was performed in an hourly time resolution and a 3 km spatial resolution, with forecast lead-time of up to four days. The WRF prediction of these variables have been compared against measurements from 29 meteorological stations across Israel for the year 2013. The studied area is small but with strong climatic gradient, diverse topography and variety of synoptic conditions. The forecast skill that was used for forecast validation takes into account the prediction bias, mean absolute error and root mean squared error. The forecast skill of the variables was almost robust to lead time, except for precipitation. The forecast skill was tested across stations with respect to topography and geographic location and for all stations with respect to seasonality and synoptic weather system determined by employing a semi-objective synoptic systems classification to the forecasted days. It was noticeable that forecast skill of some of the variables was deteriorated by seasonality and topography. However, larger impacts in the ET0 skill scores on the forecasted day are achieved by a synoptic based forecast. These results set the basis for increasing the robustness of ET0 to synoptic effects and for more precise crop irrigation over Israel.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles.

PubMed

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles

PubMed Central

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words. PMID:27313605

Advanced decision modeling for real time variable tolling : development and testing of a data collection platform.

DOT National Transportation Integrated Search

2012-06-01

Our current ability to forecast demand on tolled facilities has not kept pace with advances in decision sciences and : technological innovation. The current forecasting methods suffer from lack of descriptive power of actual behavior because : of the...

Evolving forecasting classifications and applications in health forecasting

PubMed Central

Soyiri, Ireneous N; Reidpath, Daniel D

2012-01-01

Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation. PMID:22615533

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 3. Appendices to the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 2. Simulation Analysis Using the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Understanding Air Transportation Market Dynamics Using a Search Algorithm for Calibrating Travel Demand and Price

NASA Technical Reports Server (NTRS)

Kumar, Vivek; Horio, Brant M.; DeCicco, Anthony H.; Hasan, Shahab; Stouffer, Virginia L.; Smith, Jeremy C.; Guerreiro, Nelson M.

2015-01-01

This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process - Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.

Model Information Exchange System (MIXS).

DOT National Transportation Integrated Search

2013-08-01

Many travel demand forecast models operate at state, regional, and local levels. While they share the same physical network in overlapping geographic areas, they use different and uncoordinated modeling networks. This creates difficulties for models ...

Water and Power Systems Co-optimization under a High Performance Computing Framework

NASA Astrophysics Data System (ADS)

Xuan, Y.; Arumugam, S.; DeCarolis, J.; Mahinthakumar, K.

2016-12-01

Water and energy systems optimizations are traditionally being treated as two separate processes, despite their intrinsic interconnections (e.g., water is used for hydropower generation, and thermoelectric cooling requires a large amount of water withdrawal). Given the challenges of urbanization, technology uncertainty and resource constraints, and the imminent threat of climate change, a cyberinfrastructure is needed to facilitate and expedite research into the complex management of these two systems. To address these issues, we developed a High Performance Computing (HPC) framework for stochastic co-optimization of water and energy resources to inform water allocation and electricity demand. The project aims to improve conjunctive management of water and power systems under climate change by incorporating improved ensemble forecast models of streamflow and power demand. First, by downscaling and spatio-temporally disaggregating multimodel climate forecasts from General Circulation Models (GCMs), temperature and precipitation forecasts are obtained and input into multi-reservoir and power systems models. Extended from Optimus (Optimization Methods for Universal Simulators), the framework drives the multi-reservoir model and power system model, Temoa (Tools for Energy Model Optimization and Analysis), and uses Particle Swarm Optimization (PSO) algorithm to solve high dimensional stochastic problems. The utility of climate forecasts on the cost of water and power systems operations is assessed and quantified based on different forecast scenarios (i.e., no-forecast, multimodel forecast and perfect forecast). Analysis of risk management actions and renewable energy deployments will be investigated for the Catawba River basin, an area with adequate hydroclimate predicting skill and a critical basin with 11 reservoirs that supplies water and generates power for both North and South Carolina. Further research using this scalable decision supporting framework will provide understanding and elucidate the intricate and interdependent relationship between water and energy systems and enhance the security of these two critical public infrastructures.

76 FR 33398 - 60-Day Notice of Proposed Information Collection; Passport Demand Forecasting Study Phase III...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-08

...; Passport Demand Forecasting Study Phase III, 1405-0177 ACTION: Notice of request for public comments... the Paperwork Reduction Act of 1995. Title of Information Collection: Passport Demand Forecasting... Approved Collection. Originating Office: Bureau of Consular Affairs, Passport Services Office: CA/PPT. Form...

77 FR 62595 - 30-Day Notice of Proposed Information Collection: Passport Demand Forecasting Study

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-15

...: Passport Demand Forecasting Study ACTION: Notice of request for public comment and submission to OMB of... collection instrument and supporting documents, to the Office of Passport Services at Passport[email protected] . SUPPLEMENTARY INFORMATION: Title of Information Collection: Passport Demand Forecasting Study. OMB Control...

77 FR 40936 - 60-Day Notice of Proposed Information Collection: Passport Demand Forecasting Study Phase III

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-11

...: Passport Demand Forecasting Study Phase III ACTION: Notice of request for public comments. SUMMARY: The... of 1995. Title of Information Collection: Passport Demand Forecasting Study Phase III. OMB Control... Consular Affairs/Passport Services (CA/PPT) Form Number: SV-2012-0006. Respondents: A national...

Research on strategy and optimization method of PRT empty vehicles resource allocation based on traffic demand forecast

NASA Astrophysics Data System (ADS)

Xiang, Yu; Tao, Cheng

2018-05-01

During the operation of the personal rapid transit system(PRT), the empty vehicle resources is distributed unevenly because of different passenger demand. In order to maintain the balance between supply and demand, and to meet the passenger needs of the ride, PRT empty vehicle resource allocation model is constructed based on the future demand forecasted by historical demand in this paper. The improved genetic algorithm is implied in distribution of the empty vehicle which can reduce the customers waiting time and improve the operation efficiency of the PRT system so that all passengers can take the PRT vehicles in the shortest time. The experimental result shows that the improved genetic algorithm can allocate the empty vehicle from the system level optimally, and realize the distribution of the empty vehicle resources reasonably in the system.

Research on electricity consumption forecast based on mutual information and random forests algorithm

NASA Astrophysics Data System (ADS)

Shi, Jing; Shi, Yunli; Tan, Jian; Zhu, Lei; Li, Hu

2018-02-01

Traditional power forecasting models cannot efficiently take various factors into account, neither to identify the relation factors. In this paper, the mutual information in information theory and the artificial intelligence random forests algorithm are introduced into the medium and long-term electricity demand prediction. Mutual information can identify the high relation factors based on the value of average mutual information between a variety of variables and electricity demand, different industries may be highly associated with different variables. The random forests algorithm was used for building the different industries forecasting models according to the different correlation factors. The data of electricity consumption in Jiangsu Province is taken as a practical example, and the above methods are compared with the methods without regard to mutual information and the industries. The simulation results show that the above method is scientific, effective, and can provide higher prediction accuracy.

CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

NASA Astrophysics Data System (ADS)

Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

2016-09-01

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention.

A national econometric forecasting model of the dental sector.

PubMed Central

Feldstein, P J; Roehrig, C S

1980-01-01

The Econometric Model of the the Dental Sector forecasts a broad range of dental sector variables, including dental care prices; the amount of care produced and consumed; employment of hygienists, dental assistants, and clericals; hours worked by dentists; dental incomes; and number of dentists. These forecasts are based upon values specified by the user for the various factors which help determine the supply an demand for dental care, such as the size of the population, per capita income, the proportion of the population covered by private dental insurance, the cost of hiring clericals and dental assistants, and relevant government policies. In a test of its reliability, the model forecast dental sector behavior quite accurately for the period 1971 through 1977. PMID:7461974

Forecast of future aviation fuels: The model

NASA Technical Reports Server (NTRS)

Ayati, M. B.; Liu, C. Y.; English, J. M.

1981-01-01

A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.

Forecasting domestic water demand in the Haihe river basin under changing environment

NASA Astrophysics Data System (ADS)

Wang, Xiao-Jun; Zhang, Jian-Yun; Shahid, Shamsuddin; Xie, Yu-Xuan; Zhang, Xu

2018-02-01

A statistical model has been developed for forecasting domestic water demand in Haihe river basin of China due to population growth, technological advances and climate change. Historical records of domestic water use, climate, population and urbanization are used for the development of model. An ensemble of seven general circulation models (GCMs) namely, BCC-CSM1-1, BNU-ESM, CNRM-CM5, GISS-E2-R, MIROC-ESM, PI-ESM-LR, MRI-CGCM3 were used for the projection of climate and the changes in water demand in the Haihe River basin under Representative Concentration Pathways (RCPs) 4.5. The results showed that domestic water demand in different sub-basins of the Haihe river basin will gradually increase due to continuous increase of population and rise in temperature. It is projected to increase maximum 136.22 × 108 m3 by GCM BNU-ESM and the minimum 107.25 × 108 m3 by CNRM-CM5 in 2030. In spite of uncertainty in projection, it can be remarked that climate change and population growth would cause increase in water demand and consequently, reduce the gap between water supply and demand, which eventually aggravate the condition of existing water stress in the basin. Water demand management should be emphasized for adaptation to ever increasing water demand and mitigation of the impacts of environmental changes.

Time series modelling to forecast prehospital EMS demand for diabetic emergencies.

PubMed

Villani, Melanie; Earnest, Arul; Nanayakkara, Natalie; Smith, Karen; de Courten, Barbora; Zoungas, Sophia

2017-05-05

Acute diabetic emergencies are often managed by prehospital Emergency Medical Services (EMS). The projected growth in prevalence of diabetes is likely to result in rising demand for prehospital EMS that are already under pressure. The aims of this study were to model the temporal trends and provide forecasts of prehospital attendances for diabetic emergencies. A time series analysis on monthly cases of hypoglycemia and hyperglycemia was conducted using data from the Ambulance Victoria (AV) electronic database between 2009 and 2015. Using the seasonal autoregressive integrated moving average (SARIMA) modelling process, different models were evaluated. The most parsimonious model with the highest accuracy was selected. Forty-one thousand four hundred fifty-four prehospital diabetic emergencies were attended over a seven-year period with an increase in the annual median monthly caseload between 2009 (484.5) and 2015 (549.5). Hypoglycemia (70%) and people with type 1 diabetes (48%) accounted for most attendances. The SARIMA (0,1,0,12) model provided the best fit, with a MAPE of 4.2% and predicts a monthly caseload of approximately 740 by the end of 2017. Prehospital EMS demand for diabetic emergencies is increasing. SARIMA time series models are a valuable tool to allow forecasting of future caseload with high accuracy and predict increasing cases of prehospital diabetic emergencies into the future. The model generated by this study may be used by service providers to allow appropriate planning and resource allocation of EMS for diabetic emergencies.

39 CFR 3050.26 - Documentation of demand elasticities and volume forecasts.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false Documentation of demand elasticities and volume forecasts. 3050.26 Section 3050.26 Postal Service POSTAL REGULATORY COMMISSION PERSONNEL PERIODIC REPORTING § 3050.26 Documentation of demand elasticities and volume forecasts. By January 20 of each year, the Postal Service shall provide econometric...

Crowd Sourcing Approach for UAS Communication Resource Demand Forecasting

NASA Technical Reports Server (NTRS)

Wargo, Chris A.; Difelici, John; Roy, Aloke; Glaneuski, Jason; Kerczewski, Robert J.

2016-01-01

Congressional attention to Unmanned Aircraft Systems (UAS) has caused the Federal Aviation Administration (FAA) to move the National Airspace System (NAS) Integration project forward, but using guidelines, practices and procedures that are yet to be fully integrated with the FAA Aviation Management System. The real drive for change in the NAS will to come from both UAS operators and the government jointly seeing an accurate forecast of UAS usage demand data. This solid forecast information would truly get the attention of planners. This requires not an aggregate demand, but rather a picture of how the demand is spread across small to large UAS, how it is spread across a wide range of missions, how it is expected over time and where, in terms of geospatial locations, will the demand appear. In 2012 the Volpe Center performed a study of the overall future demand for UAS. This was done by aggregate classes of aircraft types. However, the realistic expected demand will appear in clusters of aircraft activities grouped by similar missions on a smaller geographical footprint and then growing from those small cells. In general, there is not a demand forecast that is tightly coupled to the real purpose of the mission requirements (e.g. in terms of real locations and physical structures such as wind mills to inspect, farms to survey, pipelines to patrol, etc.). Being able to present a solid basis for the demand is crucial to getting the attention of investment, government and other fiscal planners. To this end, Mosaic ATM under NASA guidance is developing a crowd sourced, demand forecast engine that can draw forecast details from commercial and government users and vendors. These forecasts will be vetted by a governance panel and then provide for a sharable accurate set of projection data. Our paper describes the project and the technical approach we are using to design and create access for users to the forecast system.

Understanding and Modeling Freight Stakeholder Behavior

DOT National Transportation Integrated Search

2012-04-01

This project developed a conceptual model of private-sector freight stakeholder decisions and interactions for : forecasting freight demands in response to key policy variables. Using East Central Wisconsin as a study area, empirical : models were de...

Forecasting in the presence of expectations

NASA Astrophysics Data System (ADS)

Allen, R.; Zivin, J. G.; Shrader, J.

2016-05-01

Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model-it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.

Assessing high shares of renewable energies in district heating systems - a case study for the city of Herten

NASA Astrophysics Data System (ADS)

Aydemir, Ali; Popovski, Eftim; Bellstädt, Daniel; Fleiter, Tobias; Büchele, Richard

2017-11-01

Many earlier studies have assessed the DH generation mix without taking explicitly into account future changes in the building stock and heat demand. The approach of this study consists of three steps that combine stock modeling, energy demand forecasting, and simulation of different energy technologies. First, a detailed residential building stock model for Herten is constructed by using remote sensing together with a typology for the German building stock. Second, a bottom-up simulation model is used which calculates the thermal energy demand based on energy-related investments in buildings in order to forecast the thermal demand up to 2050. Third, solar thermal fields in combination with large-scale heat pumps are sized as an alternative to the current coal-fired CHPs. We finally assess cost of heat and CO2 reduction for these units for two scenarios which differ with regard to the DH expansion. It can be concluded that up to 2030 and 2050 a substantial reduction in buildings heat demand due to the improved building insulation is expected. The falling heat demand in the DH substantially reduces the economic feasibility of new RES generation capacity. This reduction might be compensated by continuously connecting apartment buildings to the DH network until 2050.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

PubMed

Lu, Chi-Jie; Chang, Chi-Chang

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

PubMed Central

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738

Unmanned Aircraft Systems Demand Forecast Study

NASA Technical Reports Server (NTRS)

Hackenberg, Davis L.

2017-01-01

UAS demand slides discuss the purpose, scope, and assumptions of the UAS Demand Forecast Study. It discusses some operational environments and market research study, this information is broad knowledge in the UAS community.

Mathematic simulation of mining company’s power demand forecast (by example of “Neryungri” coal strip mine)

NASA Astrophysics Data System (ADS)

Antonenkov, D. V.; Solovev, D. B.

2017-10-01

The article covers the aspects of forecasting and consideration of the wholesale market environment in generating the power demand forecast. Major mining companies that operate in conditions of the present day power market have to provide a reliable energy demand request for a certain time period ahead, thus ensuring sufficient reduction of financial losses associated with deviations of the actual power demand from the expected figures. Normally, under the power supply agreement, the consumer is bound to provide a per-month and per-hour request annually. It means that the consumer has to generate one-month-ahead short-term and medium-term hourly forecasts. The authors discovered that empiric distributions of “Yakutugol”, Holding Joint Stock Company, power demand belong to the sustainable rank parameter H-distribution type used for generating forecasts based on extrapolation of such distribution parameters. For this reason they justify the need to apply the mathematic rank analysis in short-term forecasting of the contracted power demand of “Neryungri” coil strip mine being a component of the technocenosis-type system of the mining company “Yakutugol”, Holding JSC.

Why preferring parametric forecasting to nonparametric methods?

PubMed

Jabot, Franck

2015-05-07

A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.

Recreational fishing in the Southeast United States: a demand project analysis

Treesearch

Neelam C. Poudyal; J.M. Bowker

2008-01-01

The objective of this paper is to first develop an economic model of demand for recreational fishing in the Southeastern United States, and then project the demand for fishing in the region during the next few decades. The findings from this study will be useful to understand the factors behind declining people's participation and also to forecast the license...

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.

Forecasting the Emergency Department Patients Flow.

PubMed

Afilal, Mohamed; Yalaoui, Farouk; Dugardin, Frédéric; Amodeo, Lionel; Laplanche, David; Blua, Philippe

2016-07-01

Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods.

Planning Inmarsat's second generation of spacecraft

NASA Astrophysics Data System (ADS)

Williams, W. P.

1982-09-01

The next generation of studies of the Inmarsat service are outlined, such as traffic forecasting studies, communications capacity estimates, space segment design, cost estimates, and financial analysis. Traffic forecasting will require future demand estimates, and a computer model has been developed which estimates demand over the Atlantic, Pacific, and Indian ocean regions. Communications estimates are based on traffic estimates, as a model converts traffic demand into a required capacity figure for a given area. The Erlang formula is used, requiring additional data such as peak hour ratios and distribution estimates. Basic space segment technical requirements are outlined (communications payload, transponder arrangements, etc), and further design studies involve such areas as space segment configuration, launcher and spacecraft studies, transmission planning, and earth segment configurations. Cost estimates of proposed design parameters will be performed, but options must be reduced to make construction feasible. Finally, a financial analysis will be carried out in order to calculate financial returns.

Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework

NASA Astrophysics Data System (ADS)

Sankarasubramanian, A.; Lall, Upmanu; Souza Filho, Francisco Assis; Sharma, Ashish

2009-11-01

Probabilistic, seasonal to interannual streamflow forecasts are becoming increasingly available as the ability to model climate teleconnections is improving. However, water managers and practitioners have been slow to adopt such products, citing concerns with forecast skill. Essentially, a management risk is perceived in "gambling" with operations using a probabilistic forecast, while a system failure upon following existing operating policies is "protected" by the official rules or guidebook. In the presence of a prescribed system of prior allocation of releases under different storage or water availability conditions, the manager has little incentive to change. Innovation in allocation and operation is hence key to improved risk management using such forecasts. A participatory water allocation process that can effectively use probabilistic forecasts as part of an adaptive management strategy is introduced here. Users can express their demand for water through statements that cover the quantity needed at a particular reliability, the temporal distribution of the "allocation," the associated willingness to pay, and compensation in the event of contract nonperformance. The water manager then assesses feasible allocations using the probabilistic forecast that try to meet these criteria across all users. An iterative process between users and water manager could be used to formalize a set of short-term contracts that represent the resulting prioritized water allocation strategy over the operating period for which the forecast was issued. These contracts can be used to allocate water each year/season beyond long-term contracts that may have precedence. Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser optimization model that can support such an allocation process is presented. The application of this conceptual model is explored using data for the Jaguaribe Metropolitan Hydro System in Ceara, Brazil. The performance relative to the current allocation process is assessed in the context of whether such a model could support the proposed short-term contract based participatory process. A synthetic forecasting example is also used to explore the relative roles of forecast skill and reservoir storage in this framework.

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.

Forecasting Optimal Solar Energy Supply in Jiangsu Province (China): A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

PubMed Central

Zhao, Xiuli; Yiranbon, Ethel

2014-01-01

The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor. PMID:24511292

Forecasting optimal solar energy supply in Jiangsu Province (China): a systematic approach using hybrid of weather and energy forecast models.

PubMed

Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel

2014-01-01

The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, "least-cost," and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

NASA Astrophysics Data System (ADS)

Li, Xiwang

Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of this thesis can be used for building cluster energy forecasting model development and model based control and operation optimization. The thesis concludes with a summary of the key outcomes of this research, as well as a list of recommendations for future work.

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

Documentation of volume 3 of the 1978 Energy Information Administration annual report to congress

NASA Astrophysics Data System (ADS)

1980-02-01

In a preliminary overview of the projection process, the relationship between energy prices, supply, and demand is addressed. Topics treated in detail include a description of energy economic interactions, assumptions regarding world oil prices, and energy modeling in the long term beyond 1995. Subsequent sections present the general approach and methodology underlying the forecasts, and define and describe the alternative projection series and their associated assumptions. Short term forecasting, midterm forecasting, long term forecasting of petroleum, coal, and gas supplies are included. The role of nuclear power as an energy source is also discussed.

Demand Activated Manufacturing Architecture (DAMA) model for supply chain collaboration

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

CHAPMAN,LEON D.; PETERSEN,MARJORIE B.

The Demand Activated Manufacturing Architecture (DAMA) project during the last five years of work with the U.S. Integrated Textile Complex (retail, apparel, textile, and fiber sectors) has developed an inter-enterprise architecture and collaborative model for supply chains. This model will enable improved collaborative business across any supply chain. The DAMA Model for Supply Chain Collaboration is a high-level model for collaboration to achieve Demand Activated Manufacturing. The five major elements of the architecture to support collaboration are (1) activity or process, (2) information, (3) application, (4) data, and (5) infrastructure. These five elements are tied to the application of themore » DAMA architecture to three phases of collaboration - prepare, pilot, and scale. There are six collaborative activities that may be employed in this model: (1) Develop Business Planning Agreements, (2) Define Products, (3) Forecast and Plan Capacity Commitments, (4) Schedule Product and Product Delivery, (5) Expedite Production and Delivery Exceptions, and (6) Populate Supply Chain Utility. The Supply Chain Utility is a set of applications implemented to support collaborative product definition, forecast visibility, planning, scheduling, and execution. The DAMA architecture and model will be presented along with the process for implementing this DAMA model.« less

Analyzing water resources

NASA Technical Reports Server (NTRS)

1979-01-01

Report on water resources discusses problems in water measurement demand, use, and availability. Also discussed are sensing accuracies, parameter monitoring, and status of forecasting, modeling, and future measurement techniques.

An overview of the 1984 Battelle outside users payload model

NASA Astrophysics Data System (ADS)

Day, J. B.; Conlon, R. J.; Neale, D. B.; Fischer, N. H.

1984-10-01

The methodology and projections from a model for the market for non-NASA, non-DOD, reimbursable payloads from the non-Soviet bloc countries over the 1984-2000 AD time period are summarized. High and low forecast ranges were made based on demand forecasts by industrial users, NASA estimates, and other publications. The launches were assumed to be alloted to either the Shuttle or the Ariane. The greatest demand for launch services is expected to come form communications and materials processing payloads, the latter either becoming a large user or remaining a research item. The number of Shuttle payload equivalents over the reference time spanis projected as 84-194, showing the large variance that is dependent on the progress in materials processing operations.

Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

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

Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity providedmore » by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.« less

Census mapbook for transportation planning.

DOT National Transportation Integrated Search

1994-12-01

Geographic display of Census data in transportation planning and policy decisions are compiled in a report of 49 maps, depicting use of the data in applications such as travel demand model development and model validation, population forecasting, cor...

A Capacity Forecast Model for Volatile Data in Maintenance Logistics

NASA Astrophysics Data System (ADS)

Berkholz, Daniel

2009-05-01

Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintaining valuable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network. The article outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning model. This enables a quick response to maintenance orders by employing appropriate measures. The information gained through the model is then systematically applied to forecast both personnel capacities and the demand for spare parts. The improved planning reliability can support MRO service providers in shortening delivery times and reducing stock levels in order to enhance the performance of their maintenance logistics.

Demand for satellite-provided domestic communications services up to the year 2000

NASA Technical Reports Server (NTRS)

Stevenson, S.; Poley, W.; Lekan, J.; Salzman, J. A.

1984-01-01

Three fixed service telecommunications demand assessment studies were completed for NASA by The Western Union Telegraph Company and the U.S. Telephone and Telegraph Corporation. They provided forecasts of the total U.S. domestic demand, from 1980 to the year 2000, for voice, data, and video services. That portion that is technically and economically suitable for transmission by satellite systems, both large trunking systems and customer premises services (CPS) systems was also estimated. In order to provide a single set of forecasts a NASA synthesis of the above studies was conducted. The services, associated forecast techniques, and data bases employed by both contractors were examined, those elements of each judged to be the most appropriate were selected, and new forecasts were made. The demand for voice, data, and video services was first forecast in fundamental units of call-seconds, bits/year, and channels, respectively. Transmission technology characteristics and capabilities were then forecast, and the fundamental demand converted to an equivalent transmission capacity. The potential demand for satellite-provided services was found to grow by a factor of 6, from 400 to 2400 equivalent 36 MHz satellite transponders over the 20-year period. About 80 percent of this was found to be more appropriate for trunking systems and 20 percent CPS.

Demand for satellite-provided domestic communications services up to the year 2000

NASA Astrophysics Data System (ADS)

Stevenson, S.; Poley, W.; Lekan, J.; Salzman, J. A.

1984-11-01

Three fixed service telecommunications demand assessment studies were completed for NASA by The Western Union Telegraph Company and the U.S. Telephone and Telegraph Corporation. They provided forecasts of the total U.S. domestic demand, from 1980 to the year 2000, for voice, data, and video services. That portion that is technically and economically suitable for transmission by satellite systems, both large trunking systems and customer premises services (CPS) systems was also estimated. In order to provide a single set of forecasts a NASA synthesis of the above studies was conducted. The services, associated forecast techniques, and data bases employed by both contractors were examined, those elements of each judged to be the most appropriate were selected, and new forecasts were made. The demand for voice, data, and video services was first forecast in fundamental units of call-seconds, bits/year, and channels, respectively. Transmission technology characteristics and capabilities were then forecast, and the fundamental demand converted to an equivalent transmission capacity. The potential demand for satellite-provided services was found to grow by a factor of 6, from 400 to 2400 equivalent 36 MHz satellite transponders over the 20-year period. About 80 percent of this was found to be more appropriate for trunking systems and 20 percent CPS.

Energy supply and demand modeling. (Latest citations from the NTIS bibliographic database). Published Search

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

Not Available

1994-01-01

The bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (Contains 250 citations and includes a subject term index and title list.)

Energy supply and demand modeling. (Latest citations from the NTIS data base). Published Search

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

Not Available

1992-10-01

The bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (Contains 250 citations and includes a subject term index and title list.)

Energy supply and demand modeling. (Latest citations from the NTIS bibliographic database). Published Search

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

Not Available

1994-12-01

The bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (Contains 250 citations and includes a subject term index and title list.)

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

Utility usage forecasting

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

Hosking, Jonathan R. M.; Natarajan, Ramesh

The computer creates a utility demand forecast model for weather parameters by receiving a plurality of utility parameter values, wherein each received utility parameter value corresponds to a weather parameter value. Determining that a range of weather parameter values lacks a sufficient amount of corresponding received utility parameter values. Determining one or more utility parameter values that corresponds to the range of weather parameter values. Creating a model which correlates the received and the determined utility parameter values with the corresponding weather parameters values.

Concepts and Methodology for Labour Market Forecasts by Occupation and Qualification in the Context of a Flexible Labour Market.

ERIC Educational Resources Information Center

Borghans, Lex; de Grip, Andries; Heijke, Hans

The problem of planning and making labor market forecasts by occupation and qualification in the context of a constantly changing labor market was examined. The examination focused on the following topics: assumptions, benefits, and pitfalls of the labor requirement model of projecting future imbalances between labor supply and demand for certain…

Spatial Pattern Classification for More Accurate Forecasting of Variable Energy Resources

NASA Astrophysics Data System (ADS)

Novakovskaia, E.; Hayes, C.; Collier, C.

2014-12-01

The accuracy of solar and wind forecasts is becoming increasingly essential as grid operators continue to integrate additional renewable generation onto the electric grid. Forecast errors affect rate payers, grid operators, wind and solar plant maintenance crews and energy traders through increases in prices, project down time or lost revenue. While extensive and beneficial efforts were undertaken in recent years to improve physical weather models for a broad spectrum of applications these improvements have generally not been sufficient to meet the accuracy demands of system planners. For renewables, these models are often used in conjunction with additional statistical models utilizing both meteorological observations and the power generation data. Forecast accuracy can be dependent on specific weather regimes for a given location. To account for these dependencies it is important that parameterizations used in statistical models change as the regime changes. An automated tool, based on an artificial neural network model, has been developed to identify different weather regimes as they impact power output forecast accuracy at wind or solar farms. In this study, improvements in forecast accuracy were analyzed for varying time horizons for wind farms and utility-scale PV plants located in different geographical regions.

A Transportation Modeling Primer

DOT National Transportation Integrated Search

2006-06-01

This primer is intended to explain the urban transportation modeling process works, the assumptions made and the steps used to forecast travel demand for urban transportation planning. This is done in order to help to understand the process and its i...

Simplified 4-Step Transportation Planning Process For Any Sized Area

DOT National Transportation Integrated Search

1999-01-01

This paper presents a streamlined version of the Washington, D.C. region's : 4-step travel demand forecasting model. The purpose for streamlining the : model was to have a model that could: replicate the regional model, and be run : in a new s...

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.

Short-term forecasting of emergency inpatient flow.

PubMed

Abraham, Gad; Byrnes, Graham B; Bain, Christopher A

2009-05-01

Hospital managers have to manage resources effectively, while maintaining a high quality of care. For hospitals where admissions from the emergency department to the wards represent a large proportion of admissions, the ability to forecast these admissions and the resultant ward occupancy is especially useful for resource planning purposes. Since emergency admissions often compete with planned elective admissions, modeling emergency demand may result in improved elective planning as well. We compare several models for forecasting daily emergency inpatient admissions and occupancy. The models are applied to three years of daily data. By measuring their mean square error in a cross-validation framework, we find that emergency admissions are largely random, and hence, unpredictable, whereas emergency occupancy can be forecasted using a model combining regression and autoregressive integrated moving average (ARIMA) model, or a seasonal ARIMA model, for up to one week ahead. Faced with variable admissions and occupancy, hospitals must prepare a reserve capacity of beds and staff. Our approach allows estimation of the required reserve capacity.

Electric energy demand and supply prospects for California

NASA Technical Reports Server (NTRS)

Jones, H. G. M.

1978-01-01

A recent history of electricity forecasting in California is given. Dealing with forecasts and regulatory uncertainty is discussed. Graphs are presented for: (1) Los Angeles Department of Water and Power and Pacific Gas and Electric present and projected reserve margins; (2) California electricity peak demand forecast; and (3) California electricity production.

Optimization of Evaporative Demand Models for Seasonal Drought Forecasting

NASA Astrophysics Data System (ADS)

McEvoy, D.; Huntington, J. L.; Hobbins, M.

2015-12-01

Providing reliable seasonal drought forecasts continues to pose a major challenge for scientists, end-users, and the water resources and agricultural communities. Precipitation (Prcp) forecasts beyond weather time scales are largely unreliable, so exploring new avenues to improve seasonal drought prediction is necessary to move towards applications and decision-making based on seasonal forecasts. A recent study has shown that evaporative demand (E0) anomaly forecasts from the Climate Forecast System Version 2 (CFSv2) are consistently more skillful than Prcp anomaly forecasts during drought events over CONUS, and E0 drought forecasts may be particularly useful during the growing season in the farming belts of the central and Midwestern CONUS. For this recent study, we used CFSv2 reforecasts to assess the skill of E0 and of its individual drivers (temperature, humidity, wind speed, and solar radiation), using the American Society for Civil Engineers Standardized Reference Evapotranspiration (ET0) Equation. Moderate skill was found in ET0, temperature, and humidity, with lesser skill in solar radiation, and no skill in wind. Therefore, forecasts of E0 based on models with no wind or solar radiation inputs may prove to be more skillful than the ASCE ET0. For this presentation we evaluate CFSv2 E0 reforecasts (1982-2009) from three different E0 models: (1) ASCE ET0; (2) Hargreaves and Samani (ET-HS), which is estimated from maximum and minimum temperature alone; and (3) Valiantzas (ET-V), which is a modified version of the Penman method for use when wind speed data are not available (or of poor quality) and is driven only by temperature, humidity, and solar radiation. The University of Idaho's gridded meteorological data (METDATA) were used as observations to evaluate CFSv2 and also to determine if ET0, ET-HS, and ET-V identify similar historical drought periods. We focus specifically on CFSv2 lead times of one, two, and three months, and season one forecasts; which are time scales with moderate skill and are more likely to be used in hydro-climatic applications and decision-making.

A forecast of typhoid conjugate vaccine introduction and demand in typhoid endemic low- and middle-income countries to support vaccine introduction policy and decisions.

PubMed

Mogasale, Vittal; Ramani, Enusa; Park, Il Yeon; Lee, Jung Seok

2017-09-02

A Typhoid Conjugate Vaccine (TCV) is expected to acquire WHO prequalification soon, which will pave the way for its use in many low- and middle-income countries where typhoid fever is endemic. Thus it is critical to forecast future vaccine demand to ensure supply meets demand, and to facilitate vaccine policy and introduction planning. We forecasted introduction dates for countries based on specific criteria and estimated vaccine demand by year for defined vaccination strategies in 2 scenarios: rapid vaccine introduction and slow vaccine introduction. In the rapid introduction scenario, we forecasted 17 countries and India introducing TCV in the first 5 y of the vaccine's availability while in the slow introduction scenario we forecasted 4 countries and India introducing TCV in the same time period. If the vaccine is targeting infants in high-risk populations as a routine single dose, the vaccine demand peaks around 40 million doses per year under the rapid introduction scenario. Similarly, if the vaccine is targeting infants in the general population as a routine single dose, the vaccine demand increases to 160 million doses per year under the rapid introduction scenario. The demand forecast projected here is an upper bound estimate of vaccine demand, where actual demand depends on various factors such as country priorities, actual vaccine introduction, vaccination strategies, Gavi financing, costs, and overall product profile. Considering the potential role of TCV in typhoid control globally; manufacturers, policymakers, donors and financing bodies should work together to ensure vaccine access through sufficient production capacity, early WHO prequalification of the vaccine, continued Gavi financing and supportive policy.

A forecast of typhoid conjugate vaccine introduction and demand in typhoid endemic low- and middle-income countries to support vaccine introduction policy and decisions

PubMed Central

Ramani, Enusa; Park, Il Yeon; Lee, Jung Seok

2017-01-01

ABSTRACT A Typhoid Conjugate Vaccine (TCV) is expected to acquire WHO prequalification soon, which will pave the way for its use in many low- and middle-income countries where typhoid fever is endemic. Thus it is critical to forecast future vaccine demand to ensure supply meets demand, and to facilitate vaccine policy and introduction planning. We forecasted introduction dates for countries based on specific criteria and estimated vaccine demand by year for defined vaccination strategies in 2 scenarios: rapid vaccine introduction and slow vaccine introduction. In the rapid introduction scenario, we forecasted 17 countries and India introducing TCV in the first 5 y of the vaccine's availability while in the slow introduction scenario we forecasted 4 countries and India introducing TCV in the same time period. If the vaccine is targeting infants in high-risk populations as a routine single dose, the vaccine demand peaks around 40 million doses per year under the rapid introduction scenario. Similarly, if the vaccine is targeting infants in the general population as a routine single dose, the vaccine demand increases to 160 million doses per year under the rapid introduction scenario. The demand forecast projected here is an upper bound estimate of vaccine demand, where actual demand depends on various factors such as country priorities, actual vaccine introduction, vaccination strategies, Gavi financing, costs, and overall product profile. Considering the potential role of TCV in typhoid control globally; manufacturers, policymakers, donors and financing bodies should work together to ensure vaccine access through sufficient production capacity, early WHO prequalification of the vaccine, continued Gavi financing and supportive policy. PMID:28604164

Assessing development pressure in the Chesapeake Bay watershed: An evaluation of two land-use change models

USGS Publications Warehouse

Claggett, Peter; Jantz, Claire A.; Goetz, S.J.; Bisland, C.

2004-01-01

Natural resource lands in the Chesapeake Bay watershed are increasingly susceptible to conversion into developed land uses, particularly as the demand for residential development grows. We assessed development pressure in the Baltimore-Washington, DC region, one of the major urban and suburban centers in the watershed. We explored the utility of two modeling approaches for forecasting future development trends and patterns by comparing results from a cellular automata model, SLEUTH (slope, land use, excluded land, urban extent, transportation), and a supply/demand/allocation model, the Western Futures Model. SLEUTH can be classified as a land-cover change model and produces projections on the basis of historic trends of changes in the extent and patterns of developed land and future land protection scenarios. The Western Futures Model derives forecasts from historic trends in housing units, a U.S. Census variable, and exogenously supplied future population projections. Each approach has strengths and weaknesses, and combining the two has advantages and limitations. ?? 2004 Kluwer Academic Publishers.

Assessing development pressure in the Chesapeake Bay watershed: an evaluation of two land-use change models.

PubMed

Claggett, Peter R; Jantz, Claire A; Goetz, Scott J; Bisland, Carin

2004-06-01

Natural resource lands in the Chesapeake Bay watershed are increasingly susceptible to conversion into developed land uses, particularly as the demand for residential development grows. We assessed development pressure in the Baltimore-Washington, DC region, one of the major urban and suburban centers in the watershed. We explored the utility of two modeling approaches for forecasting future development trends and patterns by comparing results from a cellular automata model, SLEUTH (slope, land use, excluded land, urban extent, transportation), and a supply/demand/allocation model, the Western Futures Model. SLEUTH can be classified as a land-cover change model and produces projections on the basis of historic trends of changes in the extent and patterns of developed land and future land protection scenarios. The Western Futures Model derives forecasts from historic trends in housing units, a U.S. Census variable, and exogenously supplied future population projections. Each approach has strengths and weaknesses, and combining the two has advantages and limitations.

Demand modelling of passenger air travel: An analysis and extension, volume 2

NASA Technical Reports Server (NTRS)

Jacobson, I. D.

1978-01-01

Previous intercity travel demand models in terms of their ability to predict air travel in a useful way and the need for disaggregation in the approach to demand modelling are evaluated. The viability of incorporating non-conventional factors (i.e. non-econometric, such as time and cost) in travel demand forecasting models are determined. The investigation of existing models is carried out in order to provide insight into their strong points and shortcomings. The model is characterized as a market segmentation model. This is a consequence of the strengths of disaggregation and its natural evolution to a usable aggregate formulation. The need for this approach both pedagogically and mathematically is discussed. In addition this volume contains two appendices which should prove useful to the non-specialist in the area.

Short-Term Energy Outlook Model Documentation: Hydrocarbon Gas Liquids Supply and Demand

EIA Publications

2015-01-01

The hydrocarbon gas liquids (ethane, propane, butanes, and natural gasoline) module of the Short-Term Energy Outlook (STEO) model is designed to provide forecasts of U.S. production, consumption, refinery inputs, net imports, and inventories.

Framework for multi-resolution analyses of advanced traffic management strategies.

DOT National Transportation Integrated Search

2016-11-01

Demand forecasting models and simulation models have been developed, calibrated, and used in isolation of each other. However, the advancement of transportation system technologies and strategies, the increase in the availability of data, and the unc...

Integrated Land - Use , Transportation and Environmental Modeling : Validation Case Studies

DOT National Transportation Integrated Search

2010-08-01

For decades the transportation-planning research community has acknowledged the interactions between the evolution of our transportation systems and our land-use, and the need to unify the practices of land-use forecasting and travel-demand modeling ...

The Uniform California Earthquake Rupture Forecast, Version 2 (UCERF 2)

USGS Publications Warehouse

,

2008-01-01

California?s 35 million people live among some of the most active earthquake faults in the United States. Public safety demands credible assessments of the earthquake hazard to maintain appropriate building codes for safe construction and earthquake insurance for loss protection. Seismic hazard analysis begins with an earthquake rupture forecast?a model of probabilities that earthquakes of specified magnitudes, locations, and faulting types will occur during a specified time interval. This report describes a new earthquake rupture forecast for California developed by the 2007 Working Group on California Earthquake Probabilities (WGCEP 2007).

Climate Forecasts and Water Resource Management: Applications for a Developing Country

NASA Astrophysics Data System (ADS)

Brown, C.; Rogers, P.

2002-05-01

While the quantity of water on the planet earth is relatively constant, the demand for water is continuously increasing. Population growth leads to linear increases in water demand, and economic growth leads to further demand growth. Strzepek et al. calculate that with a United Nations mean population estimate of 8.5 billion people by 2025 and globally balanced economic growth, water use could increase by 70% over that time (Strzepek et al., 1995). For developing nations especially, supplying water for this growing demand requires the construction of new water supply infrastructure. The prospect of designing and constructing long life-span infrastructure is clouded by the uncertainty of future climate. The availability of future water resources is highly dependent on future climate. With realization of the nonstationarity of climate, responsible design emphasizes resiliency and robustness of water resource systems (IPCC, 1995; Gleick et al., 1999). Resilient systems feature multiple sources and complex transport and distribution systems, and so come at a high economic and environmental price. A less capital-intense alternative to creating resilient and robust water resource systems is the use of seasonal climate forecasts. Such forecasts provide adequate lead time and accuracy to allow water managers and water-based sectors such as agriculture or hydropower to optimize decisions for the expected water supply. This study will assess the use of seasonal climate forecasts from regional climate models as a method to improve water resource management in systems with limited water supply infrastructure

Establishing NWP capabilities in African Small Island States (SIDs)

NASA Astrophysics Data System (ADS)

Rögnvaldsson, Ólafur

2017-04-01

Íslenskar orkurannsóknir (ÍSOR), in collaboration with Belgingur Ltd. and the United Nations Economic Commission for Africa (UNECA) signed a Letter of Agreement in 2015 regarding collaboration in the "Establishing Operational Capacity for Building, Deploying and Using Numerical Weather and Seasonal Prediction Systems in Small Island States in Africa (SIDs)" project. The specific objectives of the collaboration were the following: - Build capacity of National Meteorological and Hydrology Services (NMHS) staff on the use of the WRF atmospheric model for weather and seasonal forecasting, interpretation of model results, and the use of observations to verify and improve model simulations. - Establish a platform for integrating short to medium range weather forecasts, as well as seasonal forecasts, into already existing infrastructure at NMHS and Regional Climate Centres. - Improve understanding of existing model results and forecast verification, for improving decision-making on the time scale of days to weeks. To meet these challenges the operational Weather On Demand (WOD) forecasting system, developed by Belgingur, is being installed in a number of SIDs countries (Cabo Verde, Guinea-Bissau, and Seychelles), as well as being deployed for the Pan-Africa region, with forecasts being disseminated to collaborating NMHSs.

Forecasting Pell Program Applications Using Structural Aggregate Models.

ERIC Educational Resources Information Center

Cavin, Edward S.

1995-01-01

Demand for Pell Grant financial aid has become difficult to predict when using the current microsimulation model. This paper proposes an alternative model that uses aggregate data (based on individuals' microlevel decisions and macrodata on family incomes, college costs, and opportunity wages) and avoids some limitations of simple linear models.…

Airfreight forecasting methodology and results

NASA Technical Reports Server (NTRS)

1978-01-01

A series of econometric behavioral equations was developed to explain and forecast the evolution of airfreight traffic demand for the total U.S. domestic airfreight system, the total U.S. international airfreight system, and the total scheduled international cargo traffic carried by the top 44 foreign airlines. The basic explanatory variables used in these macromodels were the real gross national products of the countries involved and a measure of relative transportation costs. The results of the econometric analysis reveal that the models explain more than 99 percent of the historical evolution of freight traffic. The long term traffic forecasts generated with these models are based on scenarios of the likely economic outlook in the United States and 31 major foreign countries.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

2015-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

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. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Predicting Global Fund grant disbursements for procurement of artemisinin-based combination therapies

PubMed Central

Cohen, Justin M; Singh, Inder; O'Brien, Megan E

2008-01-01

Background An accurate forecast of global demand is essential to stabilize the market for artemisinin-based combination therapy (ACT) and to ensure access to high-quality, life-saving medications at the lowest sustainable prices by avoiding underproduction and excessive overproduction, each of which can have negative consequences for the availability of affordable drugs. A robust forecast requires an understanding of the resources available to support procurement of these relatively expensive antimalarials, in particular from the Global Fund, at present the single largest source of ACT funding. Methods Predictive regression models estimating the timing and rate of disbursements from the Global Fund to recipient countries for each malaria grant were derived using a repeated split-sample procedure intended to avoid over-fitting. Predictions were compared against actual disbursements in a group of validation grants, and forecasts of ACT procurement extrapolated from disbursement predictions were evaluated against actual procurement in two sub-Saharan countries. Results Quarterly forecasts were correlated highly with actual smoothed disbursement rates (r = 0.987, p < 0.0001). Additionally, predicted ACT procurement, extrapolated from forecasted disbursements, was correlated strongly with actual ACT procurement supported by two grants from the Global Fund's first (r = 0.945, p < 0.0001) and fourth (r = 0.938, p < 0.0001) funding rounds. Conclusion This analysis derived predictive regression models that successfully forecasted disbursement patterning for individual Global Fund malaria grants. These results indicate the utility of this approach for demand forecasting of ACT and, potentially, for other commodities procured using funding from the Global Fund. Further validation using data from other countries in different regions and environments will be necessary to confirm its generalizability. PMID:18831742

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.

Identifying water price and population criteria for meeting future urban water demand targets

NASA Astrophysics Data System (ADS)

Ashoori, Negin; Dzombak, David A.; Small, Mitchell J.

2017-12-01

Predictive models for urban water demand can help identify the set of factors that must be satisfied in order to meet future targets for water demand. Some of the explanatory variables used in such models, such as service area population and changing temperature and rainfall rates, are outside the immediate control of water planners and managers. Others, such as water pricing and the intensity of voluntary water conservation efforts, are subject to decisions and programs implemented by the water utility. In order to understand this relationship, a multiple regression model fit to 44 years of monthly demand data (1970-2014) for Los Angeles, California was applied to predict possible future demand through 2050 under alternative scenarios for the explanatory variables: population, price, voluntary conservation efforts, and temperature and precipitation outcomes predicted by four global climate models with two CO2 emission scenarios. Future residential water demand in Los Angeles is projected to be largely driven by price and population rather than climate change and conservation. A median projection for the year 2050 indicates that residential water demand in Los Angeles will increase by approximately 36 percent, to a level of 620 million m3 per year. The Monte Carlo simulations of the fitted model for water demand were then used to find the set of conditions in the future for which water demand is predicted to be above or below the Los Angeles Department of Water and Power 2035 goal to reduce residential water demand by 25%. Results indicate that increases in price can not ensure that the 2035 water demand target can be met when population increases. Los Angeles must rely on furthering their conservation initiatives and increasing their use of stormwater capture, recycled water, and expanding their groundwater storage. The forecasting approach developed in this study can be utilized by other cities to understand the future of water demand in water-stressed areas. Improving water demand forecasts will help planners understand and optimize future investments in water supply infrastructure and related programs.

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.

Forecasting need and demand for home health care: a selective review

PubMed Central

Sharma, Rabinder K.

1980-01-01

Three models for forecasting home health care (HHC) needs are analyzed: HSA/SP model (Health Systems Agency of Southwestern Pennsylvania); Florida model (Florida State Department of Health and Rehabilitative Services); and Rhode Island model (Rhode Island Department of Community Affairs). A utilization approach to forecasting is also presented. In the HSA/SP and Florida models, need for HHC is based on a certain proportion of (a) hospital admissions and (b) patients entering HHC from other sources. The major advantage of these models is that they are relatively easy to use and explain; their major weaknesses are an imprecise definition of need and an incomplete model specification. The Rhode Island approach defines need for HHC in terms of the health status of the population as measured by chronic activity limitations. The major strengths of this approach are its explicit assumptions and its emphasis on consumer needs. The major drawback is that it requires considerable local area data. The utilization approach is based on extrapolation from observed utilization experience of the target population. Its main limitation is that it is based on current market imperfections; its major advantage is that it exposes existing deficiencies in HHC. The author concludes that each approach should be tested empirically in order to refine it, and that need and demand approaches be used jointly in the planning process. PMID:6893631

Future Economics of Liver Transplantation: A 20-Year Cost Modeling Forecast and the Prospect of Bioengineering Autologous Liver Grafts

PubMed Central

Habka, Dany; Mann, David; Landes, Ronald; Soto-Gutierrez, Alejandro

2015-01-01

During the past 20 years liver transplantation has become the definitive treatment for most severe types of liver failure and hepatocellular carcinoma, in both children and adults. In the U.S., roughly 16,000 individuals are on the liver transplant waiting list. Only 38% of them will receive a transplant due to the organ shortage. This paper explores another option: bioengineering an autologous liver graft. We developed a 20-year model projecting future demand for liver transplants, along with costs based on current technology. We compared these cost projections against projected costs to bioengineer autologous liver grafts. The model was divided into: 1) the epidemiology model forecasting the number of wait-listed patients, operated patients and postoperative patients; and 2) the treatment model forecasting costs (pre-transplant-related costs; transplant (admission)-related costs; and 10-year post-transplant-related costs) during the simulation period. The patient population was categorized using the Model for End-Stage Liver Disease score. The number of patients on the waiting list was projected to increase 23% over 20 years while the weighted average treatment costs in the pre-liver transplantation phase were forecast to increase 83% in Year 20. Projected demand for livers will increase 10% in 10 years and 23% in 20 years. Total costs of liver transplantation are forecast to increase 33% in 10 years and 81% in 20 years. By comparison, the projected cost to bioengineer autologous liver grafts is $9.7M based on current catalog prices for iPS-derived liver cells. The model projects a persistent increase in need and cost of donor livers over the next 20 years that’s constrained by a limited supply of donor livers. The number of patients who die while on the waiting list will reflect this ever-growing disparity. Currently, bioengineering autologous liver grafts is cost prohibitive. However, costs will decline rapidly with the introduction of new manufacturing strategies and economies of scale. PMID:26177505

Future Economics of Liver Transplantation: A 20-Year Cost Modeling Forecast and the Prospect of Bioengineering Autologous Liver Grafts.

PubMed

Habka, Dany; Mann, David; Landes, Ronald; Soto-Gutierrez, Alejandro

2015-01-01

During the past 20 years liver transplantation has become the definitive treatment for most severe types of liver failure and hepatocellular carcinoma, in both children and adults. In the U.S., roughly 16,000 individuals are on the liver transplant waiting list. Only 38% of them will receive a transplant due to the organ shortage. This paper explores another option: bioengineering an autologous liver graft. We developed a 20-year model projecting future demand for liver transplants, along with costs based on current technology. We compared these cost projections against projected costs to bioengineer autologous liver grafts. The model was divided into: 1) the epidemiology model forecasting the number of wait-listed patients, operated patients and postoperative patients; and 2) the treatment model forecasting costs (pre-transplant-related costs; transplant (admission)-related costs; and 10-year post-transplant-related costs) during the simulation period. The patient population was categorized using the Model for End-Stage Liver Disease score. The number of patients on the waiting list was projected to increase 23% over 20 years while the weighted average treatment costs in the pre-liver transplantation phase were forecast to increase 83% in Year 20. Projected demand for livers will increase 10% in 10 years and 23% in 20 years. Total costs of liver transplantation are forecast to increase 33% in 10 years and 81% in 20 years. By comparison, the projected cost to bioengineer autologous liver grafts is $9.7M based on current catalog prices for iPS-derived liver cells. The model projects a persistent increase in need and cost of donor livers over the next 20 years that's constrained by a limited supply of donor livers. The number of patients who die while on the waiting list will reflect this ever-growing disparity. Currently, bioengineering autologous liver grafts is cost prohibitive. However, costs will decline rapidly with the introduction of new manufacturing strategies and economies of scale.

Combination of synoptical-analogous and dynamical methods to increase skill score of monthly air temperature forecasts over Northern Eurasia

NASA Astrophysics Data System (ADS)

Khan, Valentina; Tscepelev, Valery; Vilfand, Roman; Kulikova, Irina; Kruglova, Ekaterina; Tischenko, Vladimir

2016-04-01

Long-range forecasts at monthly-seasonal time scale are in great demand of socio-economic sectors for exploiting climate-related risks and opportunities. At the same time, the quality of long-range forecasts is not fully responding to user application necessities. Different approaches, including combination of different prognostic models, are used in forecast centers to increase the prediction skill for specific regions and globally. In the present study, two forecasting methods are considered which are exploited in operational practice of Hydrometeorological Center of Russia. One of them is synoptical-analogous method of forecasting of surface air temperature at monthly scale. Another one is dynamical system based on the global semi-Lagrangian model SL-AV, developed in collaboration of Institute of Numerical Mathematics and Hydrometeorological Centre of Russia. The seasonal version of this model has been used to issue global and regional forecasts at monthly-seasonal time scales. This study presents results of the evaluation of surface air temperature forecasts generated with using above mentioned synoptical-statistical and dynamical models, and their combination to potentially increase skill score over Northern Eurasia. The test sample of operational forecasts is encompassing period from 2010 through 2015. The seasonal and interannual variability of skill scores of these methods has been discussed. It was noticed that the quality of all forecasts is highly dependent on the inertia of macro-circulation processes. The skill scores of forecasts are decreasing during significant alterations of synoptical fields for both dynamical and empirical schemes. Procedure of combination of forecasts from different methods, in some cases, has demonstrated its effectiveness. For this study the support has been provided by Grant of Russian Science Foundation (№14-37-00053).

Land use and water use in the Antelope Valley, California

USGS Publications Warehouse

Templin, William E.; Phillips, Steven P.; Cherry, Daniel E.; DeBortoli, Myrna L.; Haltom, T.C.; McPherson, Kelly R.; Mrozek, C.A.

1995-01-01

Urban land use and water use in the Antelope Valley, California, have increased significantly since development of the valley began in the late 1800's.. Ground water has been a major source of water in this area because of limited local surface-water resources. Ground-water pumpage is reported to have increased from about 29,000 acre-feet in 1919 to about 400,000 acre-feet in the 1950's. Completion of the California Aqueduct to this area in the early 1970's conveyed water from the Sacramento-San Joaquin Delta, about 400 miles to the north. Declines in groundwater levels and increased costs of electrical power in the 1970's resulted in a reduction in the quantity of ground water that was pumped annually for irrigation uses. Total annual reported ground-water pumpage decreased to a low of about 53,200 acre-feet in 1983 and increased to about 91,700 acre-feet in 1991 as a result of rapid urban development and the 1987-92 drought. This increased urban development, in combination with several years of drought, renewed concern about a possible return to extensive depletion of ground-water storage and increased land subsidence.Increased water demands are expected to continue as a result of increased urban development. Water-demand forecasts in 1980 for the Antelope Valley indicated that total annual water demand by 2020 was expected to be about 250,000 acre-feet, with agricultural demand being about 65 percent of this total. In 1990, total water demand was projected to be about 175,000 acre-feet by 2010; however, agricultural water demand was expected to account for only 37 percent of the total demand. New and existing land- and water-use data were collected and compiled during 1992-93 to identify present and historical land and water uses. In 1993, preliminary forecasts for total water demand by 2010 ranged from about 127,500 to 329,000 acre-feet. These wide-ranging estimates indicate that forecasts can change with time as factors that affect water demand change and different forecasting methods are used. The forecasts using the MWD_MAIN (Metropolitan Water District of Southern California Municipal and Industrial Needs) water-demand forecasting system yielded the largest estimates of water demand. These forecasts were based on projections of population growth and other socioeconomic variables. Initial forecasts using the MWD_MAIN forecasting system commonly are considered "interim" or preliminary. Available historical and future socioeconomic data required for the forecasting system are limited for this area. Decisions on local water-resources demand management may be made by members of the Antelope Valley Water Group and other interested parties based on this report, other studies, their best judgement, and cumulative knowledge of local conditions. Potential water-resource management actions in the Antelope Valley include (1) increasing artificial ground-water recharge when excess local runoff (or imported water supplies) are available; (2) implementing water-conservation best-management practices; and (3) optimizing ground-water pumpage throughout the basin.

Regional demand forecasting and simulation model: user's manual. Task 4, final report

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

Parhizgari, A M

1978-09-25

The Department of Energy's Regional Demand Forecasting Model (RDFOR) is an econometric and simulation system designed to estimate annual fuel-sector-region specific consumption of energy for the US. Its purposes are to (1) provide the demand side of the Project Independence Evaluation System (PIES), (2) enhance our empirical insights into the structure of US energy demand, and (3) assist policymakers in their decisions on and formulations of various energy policies and/or scenarios. This report provides a self-contained user's manual for interpreting, utilizing, and implementing RDFOR simulation software packages. Chapters I and II present the theoretical structure and the simulation of RDFOR,more » respectively. Chapter III describes several potential scenarios which are (or have been) utilized in the RDFOR simulations. Chapter IV presents an overview of the complete software package utilized in simulation. Chapter V provides the detailed explanation and documentation of this package. The last chapter describes step-by-step implementation of the simulation package using the two scenarios detailed in Chapter III. The RDFOR model contains 14 fuels: gasoline, electricity, natural gas, distillate and residual fuels, liquid gases, jet fuel, coal, oil, petroleum products, asphalt, petroleum coke, metallurgical coal, and total fuels, spread over residential, commercial, industrial, and transportation sectors.« less

Developing Passenger Demand Models for International Aviation from/to Egypt: A Case Study of Cairo Airport and Egyptair

NASA Technical Reports Server (NTRS)

Abbas, Khaled A.; Fattah, Nabil Abdel; Reda, Hala R.

2003-01-01

This research is concerned with developing passenger demand models for international aviation from/to Egypt. In this context, aviation sector in Egypt is represented by the biggest and main airport namely Cairo airport as well as by the main Egyptian international air carrier namely Egyptair. The developed models utilize two variables to represent aviation demand, namely total number of international flights originating from and attracted to Cairo airport as well as total number of passengers using Egyptair international flights originating from and attracted to Cairo airport. Such demand variables were related, using different functional forms, to several explanatory variables including population, GDP and number of foreign tourists. Finally, two models were selected based on their logical acceptability, best fit and statistical significance. To demonstrate usefulness of developed models, these were used to forecast future demand patterns.

GIS and Transportation Planning

DOT National Transportation Integrated Search

1998-09-16

Two main objectives of transportation planning are to simulate the current : traffic volume and to forecast the future traffic volume on a transportation : network. Traffic demand modeling typically consists of the following : tasks (1)defining traff...

Development of Regional Power Sector Coal Fuel Costs (Prices) for the Short-Term Energy Outlook (STEO) Model

EIA Publications

2017-01-01

The U.S. Energy Information Administration's Short-Term Energy Outlook (STEO) produces monthly projections of energy supply, demand, trade, and prices over a 13-24 month period. Every January, the forecast horizon is extended through December of the following year. The STEO model is an integrated system of econometric regression equations and identities that link data on the various components of the U.S. energy industry together in order to develop consistent forecasts. The regression equations are estimated and the STEO model is solved using the EViews 9.5 econometric software package from IHS Global Inc. The model consists of various modules specific to each energy resource. All modules provide projections for the United States, and some modules provide more detailed forecasts for different regions of the country.

A Drought Early Warning System Using System Dynamics Model and Seasonal Climate Forecasts: a case study in Hsinchu, Taiwan.

NASA Astrophysics Data System (ADS)

Tien, Yu-Chuan; Tung, Ching-Ping; Liu, Tzu-Ming; Lin, Chia-Yu

2016-04-01

In the last twenty years, Hsinchu, a county of Taiwan, has experienced a tremendous growth in water demand due to the development of Hsinchu Science Park. In order to fulfill the water demand, the government has built the new reservoir, Baoshan second reservoir. However, short term droughts still happen. One of the reasons is that the water level of the reservoirs in Hsinchu cannot be reasonably forecasted, which sometimes even underestimates the severity of drought. The purpose of this study is to build a drought early warning system that projects the water levels of two important reservoirs, Baoshan and Baoshan second reservoir, and also the spatial distribution of water shortagewith the lead time of three months. Furthermore, this study also attempts to assist the government to improve water resources management. Hence, a system dynamics model of Touchien River, which is the most important river for public water supply in Hsinchu, is developed. The model consists of several important subsystems, including two reservoirs, water treatment plants and agricultural irrigation districts. Using the upstream flow generated by seasonal weather forecasting data, the model is able to simulate the storage of the two reservoirs and the distribution of water shortage. Moreover, the model can also provide the information under certain emergency scenarios, such as the accident or failure of a water treatment plant. At last, the performance of the proposed method and the original water resource management method that the government used were also compared. Keyword: Water Resource Management, Hydrology, Seasonal Climate Forecast, Reservoir, Early Warning, Drought

Travel Demand Modeling

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

Southworth, Frank; Garrow, Dr. Laurie

This chapter describes the principal types of both passenger and freight demand models in use today, providing a brief history of model development supported by references to a number of popular texts on the subject, and directing the reader to papers covering some of the more recent technical developments in the area. Over the past half century a variety of methods have been used to estimate and forecast travel demands, drawing concepts from economic/utility maximization theory, transportation system optimization and spatial interaction theory, using and often combining solution techniques as varied as Box-Jenkins methods, non-linear multivariate regression, non-linear mathematical programming,more » and agent-based microsimulation.« less

Energy supply and demand modeling. February 1985-March 1988 (A Bibliography from the NTIS data base). Report for February 1985-March 1988

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

Not Available

1990-06-01

This bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (This updated bibliography contains 201 citations, none of which are new entries to the previous edition.)

Energy supply and demand modeling. February 1985-March 1988 (Citations from the NTIS data base). Report for February 1985-March 1988

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

Not Available

1988-04-01

This bibliography contains citations concerning the utilization of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long-term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (This updated bibliography contains 201 citations, 129 of which are new entries to the previous edition.)

Energy supply and demand modeling. April 1988-June 1990 (A Bibliography from the NTIS data base). Report for April 1988-June 1990

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

Not Available

1990-06-01

This bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (This updated bibliography contains 200 citations, all of which are new entries to the previous edition.)

Issues in midterm analysis and forecasting 1998

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

NONE

1998-07-01

Issues in Midterm Analysis and Forecasting 1998 (Issues) presents a series of nine papers covering topics in analysis and modeling that underlie the Annual Energy Outlook 1998 (AEO98), as well as other significant issues in midterm energy markets. AEO98, DOE/EIA-0383(98), published in December 1997, presents national forecasts of energy production, demand, imports, and prices through the year 2020 for five cases -- a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. The forecasts were prepared by the Energy Information Administration (EIA), using EIA`smore » National Energy Modeling System (NEMS). The papers included in Issues describe underlying analyses for the projections in AEO98 and the forthcoming Annual Energy Outlook 1999 and for other products of EIA`s Office of Integrated Analysis and Forecasting. Their purpose is to provide public access to analytical work done in preparation for the midterm projections and other unpublished analyses. Specific topics were chosen for their relevance to current energy issues or to highlight modeling activities in NEMS. 59 figs., 44 tabs.« less

Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models.

PubMed

Luo, Li; Luo, Le; Zhang, Xinli; He, Xiaoli

2017-07-10

Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration. We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead. The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better. Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

PubMed Central

Yu, Ying; Wang, Yirui; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient. PMID:28246527

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network.

PubMed

Yu, Ying; Wang, Yirui; Gao, Shangce; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

Estimated Demand for Women's Health Services by 2020

PubMed Central

Dall, Timothy M.; Chakrabarti, Ritashree; Storm, Michael V.; Elwell, Erika C.

2013-01-01

Abstract Objective To estimate the demand for women's health care by 2020 using today's national utilization standards. Methods This descriptive study incorporated the most current national data resources to design a simulation model to create a health and economic profile for a representative sample of women from each state. Demand was determined utilizing equations about projected use of obstetrics-gynecology (ob-gyn) services. Applying patient profile and health care demand equations, we estimated the demand for providers in 2010 in each state for comparison with supply based on the 2010 American Medical Association Masterfile. U.S. Census Bureau population projections were used to project women's health care demands in 2020. Results The national demand for women's health care is forecast to grow by 6% by 2020. Most (81%) ob-gyn related services will be for women of reproductive age (18–44 years old). Growth in demand is forecast to be highest in states with the greatest population growth (Texas, Florida), where supply is currently less than adequate (western United States), and among Hispanic women. This increase in demand by 2020 will translate into a need for physicians or nonphysician clinicians, which is clinically equivalent to 2,090 full-time ob-gyns. Conclusion Using today's national norms of ob-gyn related services, a modest growth in women's health care demands is estimated by 2020 that will require a larger provider workforce. PMID:23829185

Estimated demand for women's health services by 2020.

PubMed

Dall, Timothy M; Chakrabarti, Ritashree; Storm, Michael V; Elwell, Erika C; Rayburn, William F

2013-07-01

To estimate the demand for women's health care by 2020 using today's national utilization standards. This descriptive study incorporated the most current national data resources to design a simulation model to create a health and economic profile for a representative sample of women from each state. Demand was determined utilizing equations about projected use of obstetrics-gynecology (ob-gyn) services. Applying patient profile and health care demand equations, we estimated the demand for providers in 2010 in each state for comparison with supply based on the 2010 American Medical Association Masterfile. U.S. Census Bureau population projections were used to project women's health care demands in 2020. The national demand for women's health care is forecast to grow by 6% by 2020. Most (81%) ob-gyn related services will be for women of reproductive age (18-44 years old). Growth in demand is forecast to be highest in states with the greatest population growth (Texas, Florida), where supply is currently less than adequate (western United States), and among Hispanic women. This increase in demand by 2020 will translate into a need for physicians or nonphysician clinicians, which is clinically equivalent to 2,090 full-time ob-gyns. Using today's national norms of ob-gyn related services, a modest growth in women's health care demands is estimated by 2020 that will require a larger provider workforce.

Bicycle and pedestrian travel demand forecasting : summary of data collection activities

DOT National Transportation Integrated Search

1997-09-01

This report summarizes data collection activities performed at eight different sites in Texas urban areas. The data : were collected to help develop and test bicycle and pedestrian travel demand forecasting techniques. The : research team collected d...

Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China.

PubMed

Zhang, Lei; Zou, Zhihong; Shan, Wei

2017-06-01

Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnO 4 and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction. Copyright © 2016. Published by Elsevier B.V.

Tourism demand in the Algarve region: Evolution and forecast using SVARMA models

NASA Astrophysics Data System (ADS)

Lopes, Isabel Cristina; Soares, Filomena; Silva, Eliana Costa e.

2017-06-01

Tourism is one of the Portuguese economy's key sectors, and its relative weight has grown over recent years. The Algarve region is particularly focused on attracting foreign tourists and has built over the years a large offer of diversified hotel units. In this paper we present multivariate time series approach to forecast the number of overnight stays in hotel units (hotels, guesthouses or hostels, and tourist apartments) in Algarve. We adjust a seasonal vector autoregressive and moving averages model (SVARMA) to monthly data between 2006 and 2016. The forecast values were compared with the actual values of the overnight stays in Algarve in 2016 and led to a MAPE of 15.1% and RMSE= 53847.28. The MAPE for the Hotel series was merely 4.56%. These forecast values can be used by a hotel manager to predict their occupancy and to determine the best pricing policy.

A Small Aircraft Transportation System (SATS) Demand Model

NASA Technical Reports Server (NTRS)

Long, Dou; Lee, David; Johnson, Jesse; Kostiuk, Peter; Yackovetsky, Robert (Technical Monitor)

2001-01-01

The Small Aircraft Transportation System (SATS) demand modeling is a tool that will be useful for decision-makers to analyze SATS demands in both airport and airspace. We constructed a series of models following the general top-down, modular principles in systems engineering. There are three principal models, SATS Airport Demand Model (SATS-ADM), SATS Flight Demand Model (SATS-FDM), and LMINET-SATS. SATS-ADM models SATS operations, by aircraft type, from the forecasts in fleet, configuration and performance, utilization, and traffic mixture. Given the SATS airport operations such as the ones generated by SATS-ADM, SATS-FDM constructs the SATS origin and destination (O&D) traffic flow based on the solution of the gravity model, from which it then generates SATS flights using the Monte Carlo simulation based on the departure time-of-day profile. LMINET-SATS, an extension of LMINET, models SATS demands at airspace and airport by all aircraft operations in US The models use parameters to provide the user with flexibility and ease of use to generate SATS demand for different scenarios. Several case studies are included to illustrate the use of the models, which are useful to identify the need for a new air traffic management system to cope with SATS.

Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation

PubMed Central

Flanagan, Eleni; Siddiqui, Sauleh; Appelbaum, Jeff; Kasper, Edward K; Levin, Scott

2016-01-01

Objective To develop and prospectively evaluate a web-based tool that forecasts the daily bed need for admissions from the cardiac catheterization laboratory using routinely available clinical data within electronic medical records (EMRs). Methods The forecast model was derived using a 13-month retrospective cohort of 6384 catheterization patients. Predictor variables such as demographics, scheduled procedures, and clinical indicators mined from free-text notes were input to a multivariable logistic regression model that predicted the probability of inpatient admission. The model was embedded into a web-based application connected to the local EMR system and used to support bed management decisions. After implementation, the tool was prospectively evaluated for accuracy on a 13-month test cohort of 7029 catheterization patients. Results The forecast model predicted admission with an area under the receiver operating characteristic curve of 0.722. Daily aggregate forecasts were accurate to within one bed for 70.3% of days and within three beds for 97.5% of days during the prospective evaluation period. The web-based application housing the forecast model was used by cardiology providers in practice to estimate daily admissions from the catheterization laboratory. Discussion The forecast model identified older age, male gender, invasive procedures, coronary artery bypass grafts, and a history of congestive heart failure as qualities indicating a patient was at increased risk for admission. Diagnostic procedures and less acute clinical indicators decreased patients’ risk of admission. Despite the site-specific limitations of the model, these findings were supported by the literature. Conclusion Data-driven predictive analytics may be used to accurately forecast daily demand for inpatient beds for cardiac catheterization patients. Connecting these analytics to EMR data sources has the potential to provide advanced operational decision support. PMID:26342217

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.

Forecasting future needs and optimal allocation of medical residency positions: the Emilia-Romagna Region case study.

PubMed

Senese, Francesca; Tubertini, Paolo; Mazzocchetti, Angelina; Lodi, Andrea; Ruozi, Corrado; Grilli, Roberto

2015-01-30

Italian regional health authorities annually negotiate the number of residency grants to be financed by the National government and the number and mix of supplementary grants to be funded by the regional budget. This study provides regional decision-makers with a requirement model to forecast the future demand of specialists at the regional level. We have developed a system dynamics (SD) model that projects the evolution of the supply of medical specialists and three demand scenarios across the planning horizon (2030). Demand scenarios account for different drivers: demography, service utilization rates (ambulatory care and hospital discharges) and hospital beds. Based on the SD outputs (occupational and training gaps), a mixed integer programming (MIP) model computes potentially effective assignments of medical specialization grants for each year of the projection. To simulate the allocation of grants, we have compared how regional and national grants can be managed in order to reduce future gaps with respect to current training patterns. The allocation of 25 supplementary grants per year does not appear as effective in reducing expected occupational gaps as the re-modulation of all regional training vacancies.

Forecasting the demand for privatized transport : what economic regulators should know and why

DOT National Transportation Integrated Search

2001-09-01

While public-private partnerships in the delivery of transport infrastructures and services is expanding, there is also growing evidence of the lack of appreciation of the importance of demand forecasting in preparing and monitoring these partnership...

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.

1988/1989 household travel survey

DOT National Transportation Integrated Search

1989-07-01

The primary objectives of this study were to provide the data: (1) : to update the trip generation rates used in the Maricopa Association of Governments (MAG) travel demand forecasting process, and; (2) to validate the MAG trip distribution model. Th...

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.

Updating of states in operational hydrological models

NASA Astrophysics Data System (ADS)

Bruland, O.; Kolberg, S.; Engeland, K.; Gragne, A. S.; Liston, G.; Sand, K.; Tøfte, L.; Alfredsen, K.

2012-04-01

Operationally the main purpose of hydrological models is to provide runoff forecasts. The quality of the model state and the accuracy of the weather forecast together with the model quality define the runoff forecast quality. Input and model errors accumulate over time and may leave the model in a poor state. Usually model states can be related to observable conditions in the catchment. Updating of these states, knowing their relation to observable catchment conditions, influence directly the forecast quality. Norway is internationally in the forefront in hydropower scheduling both on short and long terms. The inflow forecasts are fundamental to this scheduling. Their quality directly influence the producers profit as they optimize hydropower production to market demand and at the same time minimize spill of water and maximize available hydraulic head. The quality of the inflow forecasts strongly depends on the quality of the models applied and the quality of the information they use. In this project the focus has been to improve the quality of the model states which the forecast is based upon. Runoff and snow storage are two observable quantities that reflect the model state and are used in this project for updating. Generally the methods used can be divided in three groups: The first re-estimates the forcing data in the updating period; the second alters the weights in the forecast ensemble; and the third directly changes the model states. The uncertainty related to the forcing data through the updating period is due to both uncertainty in the actual observation and to how well the gauging stations represent the catchment both in respect to temperatures and precipitation. The project looks at methodologies that automatically re-estimates the forcing data and tests the result against observed response. Model uncertainty is reflected in a joint distribution of model parameters estimated using the Dream algorithm.

An integrated and dynamic optimisation model for the multi-level emergency logistics network in anti-bioterrorism system

NASA Astrophysics Data System (ADS)

Liu, Ming; Zhao, Lindu

2012-08-01

Demand for emergency resources is usually uncertain and varies quickly in anti-bioterrorism system. Besides, emergency resources which had been allocated to the epidemic areas in the early rescue cycle will affect the demand later. In this article, an integrated and dynamic optimisation model with time-varying demand based on the epidemic diffusion rule is constructed. The heuristic algorithm coupled with the MATLAB mathematical programming solver is adopted to solve the optimisation model. In what follows, the application of the optimisation model as well as a short sensitivity analysis of the key parameters in the time-varying demand forecast model is presented. The results show that both the model and the solution algorithm are useful in practice, and both objectives of inventory level and emergency rescue cost can be controlled effectively. Thus, it can provide some guidelines for decision makers when coping with emergency rescue problem with uncertain demand, and offers an excellent reference when issues pertain to bioterrorism.

International Oil Supplies and Demands

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

Not Available

1992-04-01

The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--1990 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single viewmore » of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.« less

International Oil Supplies and Demands

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

Not Available

1991-09-01

The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--90 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single viewmore » of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.« less

Workforce Projections 2010-2020: Annual Supply and Demand Forecasting Models for Physical Therapists Across the United States.

PubMed

Landry, Michel D; Hack, Laurita M; Coulson, Elizabeth; Freburger, Janet; Johnson, Michael P; Katz, Richard; Kerwin, Joanne; Smith, Megan H; Wessman, Henry C Bud; Venskus, Diana G; Sinnott, Patricia L; Goldstein, Marc

2016-01-01

Health human resources continue to emerge as a critical health policy issue across the United States. The purpose of this study was to develop a strategy for modeling future workforce projections to serve as a basis for analyzing annual supply of and demand for physical therapists across the United States into 2020. A traditional stock-and-flow methodology or model was developed and populated with publicly available data to produce estimates of supply and demand for physical therapists by 2020. Supply was determined by adding the estimated number of physical therapists and the approximation of new graduates to the number of physical therapists who immigrated, minus US graduates who never passed the licensure examination, and an estimated attrition rate in any given year. Demand was determined by using projected US population with health care insurance multiplied by a demand ratio in any given year. The difference between projected supply and demand represented a shortage or surplus of physical therapists. Three separate projection models were developed based on best available data in the years 2011, 2012, and 2013, respectively. Based on these projections, demand for physical therapists in the United States outstrips supply under most assumptions. Workforce projection methodology research is based on assumptions using imperfect data; therefore, the results must be interpreted in terms of overall trends rather than as precise actuarial data-generated absolute numbers from specified forecasting. Outcomes of this projection study provide a foundation for discussion and debate regarding the most effective and efficient ways to influence supply-side variables so as to position physical therapists to meet current and future population demand. Attrition rates or permanent exits out of the profession can have important supply-side effects and appear to have an effect on predicting future shortage or surplus of physical therapists. © 2016 American Physical Therapy Association.

Understanding the Influence of Climate Forecasts on Farmer Decisions as Planned Behavior

NASA Astrophysics Data System (ADS)

Artikov, Ikrom; Hoffman, Stacey J.; Lynne, Gary D.; Pytlik Zillig, Lisa M.; Hu, Qi; Tomkins, Alan J.; Hubbard, Kenneth G.; Hayes, Michael J.; Waltman, William

2006-09-01

Results of a set of four regression models applied to recent survey data of farmers in eastern Nebraska suggest the causes that drive farmer intentions of using weather and climate information and forecasts in farming decisions. The model results quantify the relative importance of attitude, social norm, perceived behavioral control, and financial capability in explaining the influence of climate-conditions information and short-term and long-term forecasts on agronomic, crop insurance, and crop marketing decisions. Attitude, serving as a proxy for the utility gained from the use of such information, had the most profound positive influence on the outcome of all the decisions, followed by norms. The norms in the community, as a proxy for the utility gained from allowing oneself to be influenced by others, played a larger role in agronomic decisions than in insurance or marketing decisions. In addition, the interaction of controllability (accuracy, availability, reliability, timeliness of weather and climate information), self-efficacy (farmer ability and understanding), and general preference for control was shown to be a substantive cause. Yet control variables also have an economic side: The farm-sales variable as a measure of financial ability and motivation intensified and clarified the role of control while also enhancing the statistical robustness of the attitude and norms variables in better clarifying how they drive the influence. Overall, the integrated model of planned behavior from social psychology and derived demand from economics, that is, the “planned demand model,” is more powerful than models based on either of these approaches alone. Taken together, these results suggest that the “human dimension” needs to be better recognized so as to improve effective use of climate and weather forecasts and information for farming decision making.

Urban flood early warning systems: approaches to hydrometeorological forecasting and communicating risk

NASA Astrophysics Data System (ADS)

Cranston, Michael; Speight, Linda; Maxey, Richard; Tavendale, Amy; Buchanan, Peter

2015-04-01

One of the main challenges for the flood forecasting community remains the provision of reliable early warnings of surface (or pluvial) flooding. The Scottish Flood Forecasting Service has been developing approaches for forecasting the risk of surface water flooding including capitalising on the latest developments in quantitative precipitation forecasting from the Met Office. A probabilistic Heavy Rainfall Alert decision support tool helps operational forecasters assess the likelihood of surface water flooding against regional rainfall depth-duration estimates from MOGREPS-UK linked to historical short-duration flooding in Scotland. The surface water flood risk is communicated through the daily Flood Guidance Statement to emergency responders. A more recent development is an innovative risk-based hydrometeorological approach that links 24-hour ensemble rainfall forecasts through a hydrological model (Grid-to-Grid) to a library of impact assessments (Speight et al., 2015). The early warning tool - FEWS Glasgow - presents the risk of flooding to people, property and transport across a 1km grid over the city of Glasgow with a lead time of 24 hours. Communication of the risk was presented in a bespoke surface water flood forecast product designed based on emergency responder requirements and trialled during the 2014 Commonwealth Games in Glasgow. The development of new approaches to surface water flood forecasting are leading to improved methods of communicating the risk and better performance in early warning with a reduction in false alarm rates with summer flood guidance in 2014 (67%) compared to 2013 (81%) - although verification of instances of surface water flooding remains difficult. However the introduction of more demanding hydrometeorological capabilities with associated greater levels of uncertainty does lead to an increased demand on operational flood forecasting skills and resources. Speight, L., Cole, S.J., Moore, R.J., Pierce, C., Wright, B., Golding, B., Cranston, M., Tavendale, A., Ghimire, S., and Dhondia, J. (2015) Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow. Journal of Flood Risk Management, In Press.

Forecasting international tourism demand from the US, Japan and South Korea to Malaysia: A SARIMA approach

NASA Astrophysics Data System (ADS)

Borhan, Nurbaizura; Arsad, Zainudin

2014-07-01

One of the major contributing sectors for Malaysia's economic growth is tourism. The number of international tourist arrivals to Malaysia has been showing an upward trend as a result of several programs and promotion introduced by the Malaysian government to attract international tourists to the country. This study attempts to model and to forecast tourism demand for Malaysia by three selected countries: the US, Japan and South Korea. This study utilized monthly time series data for the period from January 1999 to December 2012 and employed the well-known Box-Jenkins seasonal ARIMA modeling procedures. Not surprisingly the results show the number of tourist arrivals from the three countries contain strong seasonal component as the arrivals strongly dependent on the season in the country of origin. The findings of the study also show that the number of tourist arrivals from the US and South Korea will continue to increase in the near future. Meanwhile the arrivals from Japan is forecasted to show a drop in the near future and as such tourism authorities in Malaysia need to enhance the promotional effort to attract more tourists from Japan to visit Malaysia.

Integrated Mode Choice, Small Aircraft Demand, and Airport Operations Model User's Guide

NASA Technical Reports Server (NTRS)

Yackovetsky, Robert E. (Technical Monitor); Dollyhigh, Samuel M.

2004-01-01

A mode choice model that generates on-demand air travel forecasts at a set of GA airports based on changes in economic characteristics, vehicle performance characteristics such as speed and cost, and demographic trends has been integrated with a model to generate itinerate aircraft operations by airplane category at a set of 3227 airports. Numerous intermediate outputs can be generated, such as the number of additional trips diverted from automobiles and schedule air by the improved performance and cost of on-demand air vehicles. The total number of transported passenger miles that are diverted is also available. From these results the number of new aircraft to service the increased demand can be calculated. Output from the models discussed is in the format to generate the origin and destination traffic flow between the 3227 airports based on solutions to a gravity model.

Numerical Model Metrics Tools in Support of Navy Operations

NASA Astrophysics Data System (ADS)

Dykes, J. D.; Fanguy, P.

2017-12-01

Increasing demands of accurate ocean forecasts that are relevant to the Navy mission decision makers demand tools that quickly provide relevant numerical model metrics to the forecasters. Increasing modelling capabilities with ever-higher resolution domains including coupled and ensemble systems as well as the increasing volume of observations and other data sources to which to compare the model output requires more tools for the forecaster to enable doing more with less. These data can be appropriately handled in a geographic information system (GIS) fused together to provide useful information and analyses, and ultimately a better understanding how the pertinent model performs based on ground truth.. Oceanographic measurements like surface elevation, profiles of temperature and salinity, and wave height can all be incorporated into a set of layers correlated to geographic information such as bathymetry and topography. In addition, an automated system that runs concurrently with the models on high performance machines matches routinely available observations to modelled values to form a database of matchups with which statistics can be calculated and displayed, to facilitate validation of forecast state and derived variables. ArcMAP, developed by Environmental Systems Research Institute, is a GIS application used by the Naval Research Laboratory (NRL) and naval operational meteorological and oceanographic centers to analyse the environment in support of a range of Navy missions. For example, acoustic propagation in the ocean is described with a three-dimensional analysis of sound speed that depends on profiles of temperature, pressure and salinity predicted by the Navy Coastal Ocean Model. The data and model output must include geo-referencing information suitable for accurately placing the data within the ArcMAP framework. NRL has developed tools that facilitate merging these geophysical data and their analyses, including intercomparisons between model predictions as well as comparison to validation data. This methodology produces new insights and facilitates identification of potential problems in ocean prediction.

Evaluation of Air Force and Navy Demand Forecasting Systems

DTIC Science & Technology

1994-01-01

forecasting approach, the Air Force Material Command is questioning the adoption of the Navy’s Statistical Demand Forecasting System ( Gitman , 1994). The...Recoverable Item Process in the Requirements Data Bank System is to manage reparable spare parts ( Gitman , 1994). Although RDB will have the capability of...D062) ( Gitman , 1994). Since a comparison is made to address Air Force concerns, this research only limits its analysis to the range of Air Force

Studies in short haul air transportation in the California corridor: Effects of design runway length; community acceptance; impact of return on investment and fuel cost increases. Volume 2: Appendices

NASA Technical Reports Server (NTRS)

Shevell, R. S.; Jones, D. W., Jr.

1973-01-01

The development of a forecast model for short haul air transportation systems in the California Corridor is discussed. The factors which determine the level of air traffic demand are identified. A forecast equation for use in airport utilization analysis is developed. A mathematical model is submitted to show the relationship between population, employment, and income for indicating future air transportation utilization. Diagrams and tables of data are included to support the conclusions reached regarding air transportation economic factors.

Projecting long term medical spending growth.

PubMed

Borger, Christine; Rutherford, Thomas F; Won, Gregory Y

2008-01-01

We present a dynamic general equilibrium model of the U.S. economy and the medical sector in which the adoption of new medical treatments is endogenous and the demand for medical services is conditional on the state of technology. We use this model to prepare 75-year medical spending forecasts and a projection of the Medicare actuarial balance, and we compare our results to those obtained from a method that has been used by government actuaries. Our baseline forecast predicts slower health spending growth in the long run and a lower Medicare actuarial deficit relative to the previous projection methodology.

Forecasting the impact of transport improvements on commuting and residential choice

NASA Astrophysics Data System (ADS)

Elhorst, J. Paul; Oosterhaven, Jan

2006-03-01

This paper develops a probabilistic, competing-destinations, assignment model that predicts changes in the spatial pattern of the working population as a result of transport improvements. The choice of residence is explained by a new non-parametric model, which represents an alternative to the popular multinominal logit model. Travel times between zones are approximated by a normal distribution function with different mean and variance for each pair of zones, whereas previous models only use average travel times. The model’s forecast error of the spatial distribution of the Dutch working population is 7% when tested on 1998 base-year data. To incorporate endogenous changes in its causal variables, an almost ideal demand system is estimated to explain the choice of transport mode, and a new economic geography inter-industry model (RAEM) is estimated to explain the spatial distribution of employment. In the application, the model is used to forecast the impact of six mutually exclusive Dutch core-periphery railway proposals in the projection year 2020.

An overview of health forecasting.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D

2013-01-01

Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.

Evaluation of the fast orthogonal search method for forecasting chloride levels in the Deltona groundwater supply (Florida, USA)

NASA Astrophysics Data System (ADS)

El-Jaat, Majda; Hulley, Michael; Tétreault, Michel

2018-02-01

Despite the broad impact and importance of saltwater intrusion in coastal aquifers, little research has been directed towards forecasting saltwater intrusion in areas where the source of saltwater is uncertain. Saline contamination in inland groundwater supplies is a concern for numerous communities in the southern US including the city of Deltona, Florida. Furthermore, conventional numerical tools for forecasting saltwater contamination are heavily dependent on reliable characterization of the physical characteristics of underlying aquifers, information that is often absent or challenging to obtain. To overcome these limitations, a reliable alternative data-driven model for forecasting salinity in a groundwater supply was developed for Deltona using the fast orthogonal search (FOS) method. FOS was applied on monthly water-demand data and corresponding chloride concentrations at water supply wells. Groundwater salinity measurements from Deltona water supply wells were applied to evaluate the forecasting capability and accuracy of the FOS model. Accurate and reliable groundwater salinity forecasting is necessary to support effective and sustainable coastal-water resource planning and management. The available (27) water supply wells for Deltona were randomly split into three test groups for the purposes of FOS model development and performance assessment. Based on four performance indices (RMSE, RSR, NSEC, and R), the FOS model proved to be a reliable and robust forecaster of groundwater salinity. FOS is relatively inexpensive to apply, is not based on rigorous physical characterization of the water supply aquifer, and yields reliable estimates of groundwater salinity in active water supply wells.

A Simple Forecasting Model Linking Macroeconomic Policy to Industrial Employment Demand.

ERIC Educational Resources Information Center

Malley, James R.; Hady, Thomas F.

A study detailed further a model linking monetary and fiscal policy to industrial employment in metropolitan and nonmetropolitan areas of four United States regions. The model was used to simulate the impacts on area and regional employment of three events in the economy: changing real gross national product (GNP) via monetary policy, holding the…

Using Terrain Analysis and Remote Sensing to Improve Snow Mass Balance and Runoff Prediction

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Coleman, A. M.; Wigmosta, M. S.

2010-12-01

Approximately 70-80% of the water in the international Columbia River basin is sourced from snowmelt. The demand for this water has competing needs, as it is used for agricultural irrigation, municipal, hydro and nuclear power generation, and environmental in-stream flow requirements. Accurate forecasting of water supply is essential for planning current needs and prediction of future demands due to growth and climate change. A significant limitation on current forecasting is spatial and temporal uncertainty in snowpack characteristics, particularly snow water equivalent. Currently, point measurements of snow mass balance are provided by the NRCS SNOTEL network. Each site consists of a snow mass sensor and meteorology station that monitors snow water equivalent, snow depth, precipitation, and temperature. There are currently 152 sites in the mountains of Oregon and Washington. An important step in improving forecasts is determining how representative each SNOTEL site is of the total mass balance of the watershed through a full accounting of the spatiotemporal variability in snowpack processes. This variation is driven by the interaction between meteorological processes, land cover, and landform. Statistical and geostatistical spatial models relate the state of the snowpack (characterized through SNOTEL, snow course measurements, and multispectral remote sensing) to terrain attributes derived from digital elevation models (elevation, aspect, slope, compound topographic index, topographic shading, etc.) and land cover. Time steps representing the progression of the snow season for several meteorologically distinct water years are investigated to identify and quantify dominant physical processes. The spatially distributed snow balance data can be used directly as model inputs to improve short- and long-range hydrologic forecasts.

MULTIREGION: a simulation-forecasting model of BEA economic area population and employment. [Bureau of Economic Analysis

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

Olsen, R.J.; Westley, G.W.; Herzog, H.W. Jr.

This report documents the development of MULTIREGION, a computer model of regional and interregional socio-economic development. The MULTIREGION model interprets the economy of each BEA economic area as a labor market, measures all activity in terms of people as members of the population (labor supply) or as employees (labor demand), and simultaneously simulates or forecasts the demands and supplies of labor in all BEA economic areas at five-year intervals. In general the outputs of MULTIREGION are intended to resemble those of the Water Resource Council's OBERS projections and to be put to similar planning and analysis purposes. This report hasmore » been written at two levels to serve the needs of multiple audiences. The body of the report serves as a fairly nontechnical overview of the entire MULTIREGION project; a series of technical appendixes provide detailed descriptions of the background empirical studies of births, deaths, migration, labor force participation, natural resource employment, manufacturing employment location, and local service employment used to construct the model.« less

Development of seasonal flow outlook model for Ganges-Brahmaputra Basins in Bangladesh

NASA Astrophysics Data System (ADS)

Hossain, Sazzad; Haque Khan, Raihanul; Gautum, Dilip Kumar; Karmaker, Ripon; Hossain, Amirul

2016-10-01

Bangladesh is crisscrossed by the branches and tributaries of three main river systems, the Ganges, Bramaputra and Meghna (GBM). The temporal variation of water availability of those rivers has an impact on the different water usages such as irrigation, urban water supply, hydropower generation, navigation etc. Thus, seasonal flow outlook can play important role in various aspects of water management. The Flood Forecasting and Warning Center (FFWC) in Bangladesh provides short term and medium term flood forecast, and there is a wide demand from end-users about seasonal flow outlook for agricultural purposes. The objective of this study is to develop a seasonal flow outlook model in Bangladesh based on rainfall forecast. It uses European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal precipitation, temperature forecast to simulate HYDROMAD hydrological model. Present study is limited for Ganges and Brahmaputra River Basins. ARIMA correction is applied to correct the model error. The performance of the model is evaluated using coefficient of determination (R2) and Nash-Sutcliffe Efficiency (NSE). The model result shows good performance with R2 value of 0.78 and NSE of 0.61 for the Brahmaputra River Basin, and R2 value of 0.72 and NSE of 0.59 for the Ganges River Basin for the period of May to July 2015. The result of the study indicates strong potential to make seasonal outlook to be operationalized.

76 FR 53704 - 30-Day Notice of Proposed Information Collection: Passport Demand Forecasting Study Phase III...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-29

...: Passport Demand Forecasting Study Phase III, OMB Number 1405-0177 ACTION: Notice of request for public... approval in accordance with the Paperwork Reduction Act of 1995. Title of Information Collection: Passport... Passport Services CA/PPT. Form Number: SV2011-0010. [[Page 53705

A supply model for nurse workforce projection in Malaysia.

PubMed

Abas, Zuraida Abal; Ramli, Mohamad Raziff; Desa, Mohamad Ishak; Saleh, Nordin; Hanafiah, Ainul Nadziha; Aziz, Nuraini; Abidin, Zaheera Zainal; Shibghatullah, Abdul Samad; Rahman, Ahmad Fadzli Nizam Abdul; Musa, Haslinda

2017-08-18

The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.

International Oil Supplies and Demands. Volume 1

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

Not Available

1991-09-01

The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--90 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world`s dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single viewmore » of the likely future path for oil prices. The model results guided the group`s thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.« less

International Oil Supplies and Demands. Volume 2

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

Not Available

1992-04-01

The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--1990 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world`s dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single viewmore » of the likely future path for oil prices. The model results guided the group`s thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.« less

Long-range forecasts for the energy market - a case study

NASA Astrophysics Data System (ADS)

Hyvärinen, Otto; Mäkelä, Antti; Kämäräinen, Matti; Gregow, Hilppa

2017-04-01

We examined the feasibility of long-range forecasts of temperature for needs of the energy sector in Helsinki, Finland. The work was done jointly by Finnish Meteorological Institute (FMI) and Helen Ltd, the main Helsinki metropolitan area energy provider, and especially provider of district heating and cooling. Because temperatures govern the need of heating and cooling and, therefore, the energy demand, better long-range forecasts of temperature would be highly useful for Helen Ltd. Heating degree day (HDD) is a parameter that indicates the demand of energy to heat a building. We examined the forecasted monthly HDD values for Helsinki using UK Met Office seasonal forecasts with the lead time up to two months. The long-range forecasts of monthly HDD showed some skill in Helsinki in winter 2015-2016, especially if the very cold January is excluded.

Assessing air quality and climate impacts of future ground freight choice in United States

NASA Astrophysics Data System (ADS)

Liu, L.; Bond, T. C.; Smith, S.; Lee, B.; Ouyang, Y.; Hwang, T.; Barkan, C.; Lee, S.; Daenzer, K.

2013-12-01

The demand for freight transportation has continued to increase due to the growth of domestic and international trade. Emissions from ground freight (truck and railways) account for around 7% of the greenhouse gas emissions, 4% of the primary particulate matter emission and 25% of the NOx emissions in the U.S. Freight railways are generally more fuel efficient than trucks and cause less congestion. Freight demand and emissions are affected by many factors, including economic activity, the spatial distribution of demand, freight modal choice and routing decision, and the technology used in each modal type. This work links these four critical aspects of freight emission system to project the spatial distribution of emissions and pollutant concentration from ground freight transport in the U.S. between 2010 and 2050. Macroeconomic scenarios are used to forecast economic activities. Future spatial structure of employment and commodity demand in major metropolitan areas are estimated using spatial models and a shift-share model, respectively. Freight flow concentration and congestion patterns in inter-regional transportation networks are predicted from a four-step freight demand forecasting model. An asymptotic vehicle routing model is also developed to estimate delivery ton-miles for intra-regional freight shipment in metropolitan areas. Projected freight activities are then converted into impacts on air quality and climate. CO2 emissions are determined using a simple model of freight activity and fuel efficiency, and compared with the projected CO2 emissions from the Second Generation Model. Emissions of air pollutants including PM, NOx and CO are calculated with a vehicle fleet model SPEW-Trend, which incorporates the dynamic change of technologies. Emissions are projected under three economic scenarios to represent different plausible futures. Pollutant concentrations are then estimated using tagged chemical tracers in an atmospheric model with the emissions serving as input.

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.

Modeling Periodic Impulsive Effects on Online TV Series Diffusion.

PubMed

Fu, Peihua; Zhu, Anding; Fang, Qiwen; Wang, Xi

Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public social communities also represents a highly correlated analysis tool to evaluate the advertising value of TV series.

Modeling Periodic Impulsive Effects on Online TV Series Diffusion

PubMed Central

Fang, Qiwen; Wang, Xi

2016-01-01

Background Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. Methods We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. Results We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. Conclusion To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public social communities also represents a highly correlated analysis tool to evaluate the advertising value of TV series. PMID:27669520

Consumption trend analysis in the industrial sector: Existing forecasts

NASA Astrophysics Data System (ADS)

1981-08-01

The Gas Research Institute (GRI) is engaged in medium- to long-range research and development in various sectors of the economy that depend on gasing technologies and equipment. To assess the potential demand for natural gas in the industrial sector, forecasts available from private and public sources were compared and analyzed. More than 20 projections were examined, and 10 of the most appropriate long-range demand forecasts were analyzed and compared with respect to the various assumptions, methodologies and criteria on which each was based.

Synthesis of traveler choice research: improving modeling accuracy for better transportation decisionmaking.

DOT National Transportation Integrated Search

2013-08-01

"Over the last 50 years, advances in the fields of travel behavior research and travel demand forecasting have been : immense, driven by the increasing costs of infrastructure and spatial limitations in areas of high population density : together wit...

MAG traffic generator study : survey data from Arizona State University

DOT National Transportation Integrated Search

1994-12-01

The Maricopa Association of Governments (MAG) is responsible for the travel demand models used to forecast multi-modal travel behavior in the Phoenix metropolitan area. The main campus of Arizona State University (ASU), located in Tempe, is one of th...

TCRP H-37 Characteristics of Premium Transit Services That Affect Mode Choice: Summary of Phase 1

DOT National Transportation Integrated Search

2010-11-15

This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models...

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

NASA Technical Reports Server (NTRS)

Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2015-01-01

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

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.

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China.

PubMed

Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

2017-01-01

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

PubMed Central

Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

2017-01-01

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry. PMID:28459872

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.

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

Non-urban mobile radio market demand forecast

NASA Technical Reports Server (NTRS)

Castruccio, P. A.; Cooper, J.

1982-01-01

A national nonmetropolitan land mobile traffic model for 1990-2000 addresses user classes, density classes, traffic mix statistics, distance distribution, geographic distribution, price elasticity, and service quality elasticity. Traffic demands for business, special industrial, and police were determined on the basis of surveys in 73 randomly selected nonurban counties. The selected services represent 69% of total demand. The results were extrapolated to all services in the non-SMSA areas of the contiguous United States. Radiotelephone services were considered separately. Total non-SMSA mobile radio demand (one way) estimates are given. General functional requirements include: hand portability, privacy, reduction of blind spots, two way data transmission, position location, slow scan imagery.

Benefits of an ultra large and multiresolution ensemble for estimating available wind power

NASA Astrophysics Data System (ADS)

Berndt, Jonas; Hoppe, Charlotte; Elbern, Hendrik

2016-04-01

In this study we investigate the benefits of an ultra large ensemble with up to 1000 members including multiple nesting with a target horizontal resolution of 1 km. The ensemble shall be used as a basis to detect events of extreme errors in wind power forecasting. Forecast value is the wind vector at wind turbine hub height (~ 100 m) in the short range (1 to 24 hour). Current wind power forecast systems rest already on NWP ensemble models. However, only calibrated ensembles from meteorological institutions serve as input so far, with limited spatial resolution (˜10 - 80 km) and member number (˜ 50). Perturbations related to the specific merits of wind power production are yet missing. Thus, single extreme error events which are not detected by such ensemble power forecasts occur infrequently. The numerical forecast model used in this study is the Weather Research and Forecasting Model (WRF). Model uncertainties are represented by stochastic parametrization of sub-grid processes via stochastically perturbed parametrization tendencies and in conjunction via the complementary stochastic kinetic-energy backscatter scheme already provided by WRF. We perform continuous ensemble updates by comparing each ensemble member with available observations using a sequential importance resampling filter to improve the model accuracy while maintaining ensemble spread. Additionally, we use different ensemble systems from global models (ECMWF and GFS) as input and boundary conditions to capture different synoptic conditions. Critical weather situations which are connected to extreme error events are located and corresponding perturbation techniques are applied. The demanding computational effort is overcome by utilising the supercomputer JUQUEEN at the Forschungszentrum Juelich.

Seasonal forecasts of impact-relevant climate information indices developed as part of the EUPORIAS project

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas

2015-04-01

Climate information indices (CIIs) represent a way to communicate climate conditions to specific sectors and the public. As such, CIIs provide actionable information to stakeholders in an efficient way. Due to their non-linear nature, such CIIs can behave differently than the underlying variables, such as temperature. At the same time, CIIs do not involve impact models with different sources of uncertainties. As part of the EU project EUPORIAS (EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale) we have developed examples of seasonal forecasts of CIIs. We present forecasts and analyses of the skill of seasonal forecasts for CIIs that are relevant to a variety of economic sectors and a range of stakeholders: heating and cooling degree days as proxies for energy demand, various precipitation and drought-related measures relevant to agriculture and hydrology, a wild fire index, a climate-driven mortality index and wind-related indices tailored to renewable energy producers. Common to all examples is the finding of limited forecast skill over Europe, highlighting the challenge for providing added-value services to stakeholders operating in Europe. The reasons for the lack of forecast skill vary: often we find little skill in the underlying variable(s) precisely in those areas that are relevant for the CII, in other cases the nature of the CII is particularly demanding for predictions, as seen in the case of counting measures such as frost days or cool nights. On the other hand, several results suggest there may be some predictability in sub-regions for certain indices. Several of the exemplary analyses show potential for skillful forecasts and prospect for improvements by investing in post-processing. Furthermore, those cases for which CII forecasts showed similar skill values as those of the underlying meteorological variables, forecasts of CIIs provide added value from a user perspective.

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.

Essays in energy economics: The electricity industry

NASA Astrophysics Data System (ADS)

Martinez-Chombo, Eduardo

Electricity demand analysis using cointegration and error-correction models with time varying parameters: The Mexican case. In this essay we show how some flexibility can be allowed in modeling the parameters of the electricity demand function by employing the time varying coefficient (TVC) cointegrating model developed by Park and Hahn (1999). With the income elasticity of electricity demand modeled as a TVC, we perform tests to examine the adequacy of the proposed model against the cointegrating regression with fixed coefficients, as well as against the spuriousness of the regression with TVC. The results reject the specification of the model with fixed coefficients and favor the proposed model. We also show how some flexibility is gained in the specification of the error correction model based on the proposed TVC cointegrating model, by including more lags of the error correction term as predetermined variables. Finally, we present the results of some out-of-sample forecast comparison among competing models. Electricity demand and supply in Mexico. In this essay we present a simplified model of the Mexican electricity transmission network. We use the model to approximate the marginal cost of supplying electricity to consumers in different locations and at different times of the year. We examine how costs and system operations will be affected by proposed investments in generation and transmission capacity given a forecast of growth in regional electricity demands. Decomposing electricity prices with jumps. In this essay we propose a model that decomposes electricity prices into two independent stochastic processes: one that represents the "normal" pattern of electricity prices and the other that captures temporary shocks, or "jumps", with non-lasting effects in the market. Each contains specific mean reverting parameters to estimate. In order to identify such components we specify a state-space model with regime switching. Using Kim's (1994) filtering algorithm we estimate the parameters of the model, the transition probabilities and the unobservable components for the mean adjusted series of New South Wales' electricity prices. Finally, bootstrap simulations were performed to estimate the expected contribution of each of the components in the overall electricity prices.

Incorporating probabilistic seasonal climate forecasts into river management using a risk-based framework

USGS Publications Warehouse

Sojda, Richard S.; Towler, Erin; Roberts, Mike; Rajagopalan, Balaji

2013-01-01

[1] Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk-based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision-relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season-ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end-to-end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very “sharp”). Synthetic forecasts show that a modest “sharpening” can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement.

Statistical Forecasting of Bankruptcy of Defense Contractors. Problems and Prospects

DTIC Science & Technology

1994-01-01

investors is along the lines of the Capital Asset Pricing Model ( CAPM ). In portfolio theory generally, investors demand an expected-return premium for...Ellen Pint, Rachel Schmidt, and especially Dennis Smallwood of RAND also contributed useful insights and comments. xv Acronyms CAPM Capital Asset ...Bond Yields ............................................. 26 Bond Model Performance ................................. 27 Extensions and Limitations

TBEST model enhancements : parcel level demographic data capabilities and exploration of enhanced trip attraction capabilities.

DOT National Transportation Integrated Search

2011-09-01

"FDOT, in pursuit of its role to assist in providing public transportation services in Florida, has made a substantial : research investment in a travel demand forecasting tool for public transportation known as Transit Boardings : Estimation and Sim...

Global operational hydrological forecasts through eWaterCycle

NASA Astrophysics Data System (ADS)

van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

2015-04-01

Central goal of the eWaterCycle project (www.ewatercycle.org) is the development of an operational hyper-resolution hydrological global model. This model is able to produce 14 day ensemble forecasts based on a hydrological model and operational weather data (presently NOAA's Global Ensemble Forecast System). Special attention is paid to prediction of situations in which water related issues are relevant, such as floods, droughts, navigation, hydropower generation, and irrigation stress. Near-real time satellite data will be assimilated in the hydrological simulations, which is a feature that will be presented for the first time at EGU 2015. First, we address challenges that are mainly computer science oriented but have direct practical hydrological implications. An important feature in this is the use of existing standards and open-source software to the maximum extent possible. For example, we use the Community Surface Dynamics Modeling System (CSDMS) approach to coupling models (Basic Model Interface (BMI)). The hydrological model underlying the project is PCR-GLOBWB, built by Utrecht University. This is the motor behind the predictions and state estimations. Parts of PCR-GLOBWB have been re-engineered to facilitate running it in a High Performance Computing (HPC) environment, run parallel on multiple nodes, as well as to use BMI. Hydrological models are not very CPU intensive compared to, say, atmospheric models. They are, however, memory hungry due to the localized processes and associated effective parameters. To accommodate this memory need, especially in an ensemble setting, a variation on the traditional Ensemble Kalman Filter was developed that needs much less on-chip memory. Due to the operational nature, the coupling of the hydrological model with hydraulic models is very important. The idea is not to run detailed hydraulic routing schemes over the complete globe but to have on-demand simulation prepared off-line with respect to topography and parameterizations. This allows for very detailed simulations at hectare to meter scales, where and when this is needed. At EGU 2015, the operational global eWaterCycle model will be presented for the first time, including forecasts at high resolution, the innovative data assimilation approach, and on-demand coupling with hydraulic models.

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.

The Value of Seasonal Climate Forecasts in Managing Energy Resources.

NASA Astrophysics Data System (ADS)

Brown Weiss, Edith

1982-04-01

Research and interviews with officials of the United States energy industry and a systems analysis of decision making in a natural gas utility lead to the conclusion that seasonal climate forecasts would only have limited value in fine tuning the management of energy supply, even if the forecasts were more reliable and detailed than at present.On the other hand, reliable forecasts could be useful to state and local governments both as a signal to adopt long-term measures to increase the efficiency of energy use and to initiate short-term measures to reduce energy demand in anticipation of a weather-induced energy crisis.To be useful for these purposes, state governments would need better data on energy demand patterns and available energy supplies, staff competent to interpret climate forecasts, and greater incentive to conserve. The use of seasonal climate forecasts is not likely to be constrained by fear of legal action by those claiming to be injured by a possible incorrect forecast.

Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data

PubMed Central

Tran, Truyen; Luo, Wei; Phung, Dinh; Venkatesh, Svetha

2016-01-01

Background: Modeling patient flow is crucial in understanding resource demand and prioritization. We study patient outflow from an open ward in an Australian hospital, where currently bed allocation is carried out by a manager relying on past experiences and looking at demand. Automatic methods that provide a reasonable estimate of total next-day discharges can aid in efficient bed management. The challenges in building such methods lie in dealing with large amounts of discharge noise introduced by the nonlinear nature of hospital procedures, and the nonavailability of real-time clinical information in wards. Objective Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. Methods We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. Results Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. Conclusions In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments. PMID:27444059

Research on time series data prediction based on clustering algorithm - A case study of Yuebao

NASA Astrophysics Data System (ADS)

Lu, Xu; Zhao, Tianzhong

2017-08-01

Forecasting is the prerequisite for making scientific decisions, it is based on the past information of the research on the phenomenon, and combined with some of the factors affecting this phenomenon, then using scientific methods to forecast the development trend of the future, it is an important way for people to know the world. This is particularly important in the prediction of financial data, because proper financial data forecasts can provide a great deal of help to financial institutions in their strategic implementation, strategic alignment and risk control. However, the current forecasts of financial data generally use the method of forecast of overall data, which lack of consideration of customer behavior and other factors in the financial data forecasting process, and they are important factors influencing the change of financial data. Based on this situation, this paper analyzed the data of Yuebao, and according to the user's attributes and the operating characteristics, this paper classified 567 users of Yuebao, and made further predicted the data of Yuebao for every class of users, the results showed that the forecasting model in this paper can meet the demand of forecasting.

Managing distrust-induced risk with deposit in supply chain contract decisions.

PubMed

Han, Guanghua; Dong, Ming; Sun, Qi

2014-01-01

This paper studies the trust issue in a two-echelon supply chain information sharing process. In a supply chain, the retailer reports the forecasted demand to the supplier. Traditionally, the supplier's trust in the retailer's reported information is based on the retailer's reputation. However, this paper considers that trust is random and is also affected by the reputation and the demand gap. The supplier and retailer have been shown to have different evaluations regarding the degree of trust. Furthermore, distrust is inherently linked to perceived risk. To mitigate perceived risk, a two-stage decision process with an unpayback deposit contract is proposed. At the first stage, the supplier and the retailer negotiate the deposit contract. At the second stage, a Stackelberg game is used to determine the retailer's reported demand and the supplier's production quantity. We show that the deposits from the retailer's and supplier's perspectives are different. When the retailer's reported demand is equal to the supplier's forecasted demand, the retailer's evaluation of the deposit is more than that of supplier's. When the retailer's reported demand is equal to the retailer's forecasted demand, the deposit from the retailer's perspective is at the lowest level.

Managing Distrust-Induced Risk with Deposit in Supply Chain Contract Decisions

PubMed Central

Han, Guanghua; Dong, Ming; Sun, Qi

2014-01-01

This paper studies the trust issue in a two-echelon supply chain information sharing process. In a supply chain, the retailer reports the forecasted demand to the supplier. Traditionally, the supplier's trust in the retailer's reported information is based on the retailer's reputation. However, this paper considers that trust is random and is also affected by the reputation and the demand gap. The supplier and retailer have been shown to have different evaluations regarding the degree of trust. Furthermore, distrust is inherently linked to perceived risk. To mitigate perceived risk, a two-stage decision process with an unpayback deposit contract is proposed. At the first stage, the supplier and the retailer negotiate the deposit contract. At the second stage, a Stackelberg game is used to determine the retailer's reported demand and the supplier's production quantity. We show that the deposits from the retailer's and supplier's perspectives are different. When the retailer's reported demand is equal to the supplier's forecasted demand, the retailer's evaluation of the deposit is more than that of supplier's. When the retailer's reported demand is equal to the retailer's forecasted demand, the deposit from the retailer's perspective is at the lowest level. PMID:25054190

The 30/20 GHz fixed communications systems service demand assessment. Volume 3: Annex

NASA Technical Reports Server (NTRS)

Gamble, R. B.; Seltzer, H. R.; Speter, K. M.; Westheimer, M.

1979-01-01

A review of studies forecasting the communication market in the United States is given. The applicability of these forecasts to assessment of demand for the 30/20 GHz fixed communications system is analyzed. Costs for the 30/20 satellite trunking systems are presented and compared with the cost of terrestrial communications.

The First Six Months: PDEM Innovations in Forecasting Higher Education, January to July 1972.

ERIC Educational Resources Information Center

Hoffman, Benjamin B.

A Postsecondary Demand Survey was undertaken in 1972 to study the demand for postsecondary education in Manitoba, Canada, and to develop a system for forecasting enrollment at postsecondary institutions. The survey objective was to establish a profile of grade 12 students in 1972 to learn about their aspirations, plans, expectations after…

Design/build vs traditional construction user delay modeling : an evaluation of the cost effectiveness of innovative construction methods for new construction. Part 2 : VISUM Online for Salt Lake, Davis, and Utah Counties

DOT National Transportation Integrated Search

2007-05-01

VISUM Online is a traffic management system for processing online traffic data. The system implements both a road network model and a traffic demand model. VISUM Online uses all available real-time and historic data to calculate current and forecaste...

Outside users payload model

NASA Technical Reports Server (NTRS)

1985-01-01

The outside users payload model which is a continuation of documents and replaces and supersedes the July 1984 edition is presented. The time period covered by this model is 1985 through 2000. The following sections are included: (1) definition of the scope of the model; (2) discussion of the methodology used; (3) overview of total demand; (4) summary of the estimated market segmentation by launch vehicle; (5) summary of the estimated market segmentation by user type; (6) details of the STS market forecast; (7) summary of transponder trends; (8) model overview by mission category; and (9) detailed mission models. All known non-NASA, non-DOD reimbursable payloads forecast to be flown by non-Soviet-block countries are included in this model with the exception of Spacelab payloads and small self contained payloads. Certain DOD-sponsored or cosponsored payloads are included if they are reimbursable launches.

Integrated Drought Monitoring and Forecasts for Decision Making in Water and Agricultural Sectors over the Southeastern US under Changing Climate

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Ward, R.

2017-12-01

Changing climate arising from structured oscillations such as ENSO and rising temperature poses challenging issues in meeting the increasing water demand (due to population growth) for public supply and agriculture over the Southeast US. This together with infrastructural (e.g., most reservoirs being within-year systems) and operational (e.g., static rule curves) constraints requires an integrated approach that seamlessly monitors and forecasts water and soil moisture conditions to support adaptive decision making in water and agricultural sectors. In this talk, we discuss the utility of an integrated drought management portal that both monitors and forecasts streamflow and soil moisture over the southeast US. The forecasts are continuously developed and updated by forcing monthly-to-seasonal climate forecasts with a land surface model for various target basins. The portal also houses a reservoir allocation model that allows water managers to explore different release policies in meeting the system constraints and target storages conditioned on the forecasts. The talk will also demonstrate how past events (e.g., 2007-2008 drought) could be proactively monitored and managed to improve decision making in water and agricultural sectors over the Southeast US. Challenges in utilizing the portal information from institutional and operational perspectives will also be presented.

Coupled hydro-meteorological modelling on a HPC platform for high-resolution extreme weather impact study

NASA Astrophysics Data System (ADS)

Zhu, Dehua; Echendu, Shirley; Xuan, Yunqing; Webster, Mike; Cluckie, Ian

2016-11-01

Impact-focused studies of extreme weather require coupling of accurate simulations of weather and climate systems and impact-measuring hydrological models which themselves demand larger computer resources. In this paper, we present a preliminary analysis of a high-performance computing (HPC)-based hydrological modelling approach, which is aimed at utilizing and maximizing HPC power resources, to support the study on extreme weather impact due to climate change. Here, four case studies are presented through implementation on the HPC Wales platform of the UK mesoscale meteorological Unified Model (UM) with high-resolution simulation suite UKV, alongside a Linux-based hydrological model, Hydrological Predictions for the Environment (HYPE). The results of this study suggest that the coupled hydro-meteorological model was still able to capture the major flood peaks, compared with the conventional gauge- or radar-driving forecast, but with the added value of much extended forecast lead time. The high-resolution rainfall estimation produced by the UKV performs similarly to that of radar rainfall products in the first 2-3 days of tested flood events, but the uncertainties particularly increased as the forecast horizon goes beyond 3 days. This study takes a step forward to identify how the online mode approach can be used, where both numerical weather prediction and the hydrological model are executed, either simultaneously or on the same hardware infrastructures, so that more effective interaction and communication can be achieved and maintained between the models. But the concluding comments are that running the entire system on a reasonably powerful HPC platform does not yet allow for real-time simulations, even without the most complex and demanding data simulation part.

Performance Improvements of the CYCOFOS Flow Model

NASA Astrophysics Data System (ADS)

Radhakrishnan, Hari; Moulitsas, Irene; Syrakos, Alexandros; Zodiatis, George; Nikolaides, Andreas; Hayes, Daniel; Georgiou, Georgios C.

2013-04-01

The CYCOFOS-Cyprus Coastal Ocean Forecasting and Observing System has been operational since early 2002, providing daily sea current, temperature, salinity and sea level forecasting data for the next 4 and 10 days to end-users in the Levantine Basin, necessary for operational application in marine safety, particularly concerning oil spills and floating objects predictions. CYCOFOS flow model, similar to most of the coastal and sub-regional operational hydrodynamic forecasting systems of the MONGOOS-Mediterranean Oceanographic Network for Global Ocean Observing System is based on the POM-Princeton Ocean Model. CYCOFOS is nested with the MyOcean Mediterranean regional forecasting data and with SKIRON and ECMWF for surface forcing. The increasing demand for higher and higher resolution data to meet coastal and offshore downstream applications motivated the parallelization of the CYCOFOS POM model. This development was carried out in the frame of the IPcycofos project, funded by the Cyprus Research Promotion Foundation. The parallel processing provides a viable solution to satisfy these demands without sacrificing accuracy or omitting any physical phenomena. Prior to IPcycofos project, there are been several attempts to parallelise the POM, as for example the MP-POM. The existing parallel code models rely on the use of specific outdated hardware architectures and associated software. The objective of the IPcycofos project is to produce an operational parallel version of the CYCOFOS POM code that can replicate the results of the serial version of the POM code used in CYCOFOS. The parallelization of the CYCOFOS POM model use Message Passing Interface-MPI, implemented on commodity computing clusters running open source software and not depending on any specialized vendor hardware. The parallel CYCOFOS POM code constructed in a modular fashion, allowing a fast re-locatable downscaled implementation. The MPI takes advantage of the Cartesian nature of the POM mesh, and use the built-in functionality of MPI routines to split the mesh, using a weighting scheme, along longitude and latitude among the processors. Each server processor work on the model based on domain decomposition techniques. The new parallel CYCOFOS POM code has been benchmarked against the serial POM version of CYCOFOS for speed, accuracy, and resolution and the results are more than satisfactory. With a higher resolution CYCOFOS Levantine model domain the forecasts need much less time than the serial CYCOFOS POM coarser version, both with identical accuracy.

Applications of remote sensing to water resources

NASA Technical Reports Server (NTRS)

1977-01-01

Analyses were made of selected long-term (1985 and beyond) objectives, with the intent of determining if significant data-related problems would be encountered and to develop alternative solutions to any potential problems. One long-term objective selected for analysis was Water Availability Forecasting. A brief overview was scheduled in FY-77 of the objective -- primarily a fact-finding study to allow Data Management personnel to gain adequate background information to perform subsequent data system analyses. This report, includes discussions on some of the larger problems currently encountered in water measurement, the potential users of water availability forecasts, projected demands of users, current sensing accuracies, required parameter monitoring, status of forecasting modeling, and some measurement accuracies likely to be achievable by 1980 and 1990.

Forecasting of monthly inflow and outflow currency using time series regression and ARIMAX: The Idul Fitri effect

NASA Astrophysics Data System (ADS)

Ahmad, Imam Safawi; Setiawan, Suhartono, Masun, Nunun Hilyatul

2015-12-01

Currency plays an important role in economic transactions of Indonesian society. In order to guarantee the availability of currency, Bank Indonesia needs to develop demand and supply planning of currency. The purpose of this study is to get model and predict inflow and outflow of currency in KPW BI Region IV (East Java) with ARIMA method, time series regression and ARIMAX. The data of monthly inflow and outflow is used of currency in KPW BI Surabaya, Malang, Kediri and Jember.The observation period starting from January 2003 to December 2014. Based on the smallest values of out-sample RMSE and SMAPE, ARIMA is the best model to predict the outflow of currency in KPW BI Surabaya and ARIMAX for KPW BI Malang, Kediri and Jember. The best forecasting model for inflow of currency in KPW BI Surabaya, Malang, Kediri and Jember chronologically as follows are calendar variation model, transfer function, ARIMA, and time series regression. These results indicates that the more complex models may not necessarily produce a more accurate forecast as the result of M3-Competition.

Forecasting runout of rock and debris avalanches

USGS Publications Warehouse

Iverson, Richard M.; Evans, S.G.; Mugnozza, G.S.; Strom, A.; Hermanns, R.L.

2006-01-01

Physically based mathematical models and statistically based empirical equations each may provide useful means of forecasting runout of rock and debris avalanches. This paper compares the foundations, strengths, and limitations of a physically based model and a statistically based forecasting method, both of which were developed to predict runout across three-dimensional topography. The chief advantage of the physically based model results from its ties to physical conservation laws and well-tested axioms of soil and rock mechanics, such as the Coulomb friction rule and effective-stress principle. The output of this model provides detailed information about the dynamics of avalanche runout, at the expense of high demands for accurate input data, numerical computation, and experimental testing. In comparison, the statistical method requires relatively modest computation and no input data except identification of prospective avalanche source areas and a range of postulated avalanche volumes. Like the physically based model, the statistical method yields maps of predicted runout, but it provides no information on runout dynamics. Although the two methods differ significantly in their structure and objectives, insights gained from one method can aid refinement of the other.

Transportation Energy Futures Series: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future

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

Grenzeback, L. R.; Brown, A.; Fischer, M. J.

2013-03-01

Freight transportation demand is projected to grow to 27.5 billion tons in 2040, and by extrapolation, to nearly 30.2 billion tons in 2050, requiring ever-greater amounts of energy. This report describes the current and future demand for freight transportation in terms of tons and ton-miles of commodities moved by truck, rail, water, pipeline, and air freight carriers. It outlines the economic, logistics, transportation, and policy and regulatory factors that shape freight demand; the possible trends and 2050 outlook for these factors, and their anticipated effect on freight demand and related energy use. After describing federal policy actions that could influencemore » freight demand, the report then summarizes the available analytical models for forecasting freight demand, and identifies possible areas for future action.« less

World Motor Vehicle Demand

DOT National Transportation Integrated Search

1982-08-01

This report discusses the level and nature of world motor vehicle demand for the period 1980-1990. A general understanding of the structure of motor vehicle demand is developed. Published demand forecasts, varying widely, are gathered and their discr...

Roads to Recovery: Three Skill and Labour Market Scenarios for 2025. Briefing Note

ERIC Educational Resources Information Center

Cedefop - European Centre for the Development of Vocational Training, 2013

2013-01-01

In line with earlier forecasts, Cedefop's projections for skill supply and demand in the European Union (EU) foresee a gradual return to job growth and an older, but better qualified workforce. The latest forecast, which is presented in this report, extends the time horizon from 2020 to 2025 and differs from its predecessors in seeing demand for…

Coping with Changes in International Classifications of Sectors and Occupations: Application in Skills Forecasting. Research Paper No 43

ERIC Educational Resources Information Center

Kvetan, Vladimir, Ed.

2014-01-01

Reliable and consistent time series are essential to any kind of economic forecasting. Skills forecasting needs to combine data from national accounts and labour force surveys, with the pan-European dimension of Cedefop's skills supply and demand forecasts, relying on different international classification standards. Sectoral classification (NACE)…

The Effect of Temperature on the Electricity Demand: An Empirical Investigation

NASA Astrophysics Data System (ADS)

Kim, H.; Kim, I. G.; Park, K. J.; Yoo, S. H.

2015-12-01

This paper attempts to estimate the electricity demand function in Korea with quarterly data of average temperature, GDP and electricity price over the period 2005-2013. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the electricity demand function. The results show that short-run price and income elasticities of the electricity demand are estimated to be -0.569 and 0.631 respectively. They are statistically significant at the 1% level. Moreover, long-run income and price elasticities are estimated to be 1.589 and -1.433 respectively. Both of results reveal that the demand for electricity demand is about 15.2℃. It is shown that power of explanation and goodness-of-fit statistics are improved in the use of the lagged dependent variable model rather than conventional model. Acknowledgements: This research was carried out as a part of "Development and application of technology for weather forecast" supported by the 2015 National Institute of Meteorological Research (NIMR) in the Korea Meteorological Administration.

Effect of initial conditions of a catchment on seasonal streamflow prediction using ensemble streamflow prediction (ESP) technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

NASA Astrophysics Data System (ADS)

Singh, Shailesh Kumar; Zammit, Christian; Hreinsson, Einar; Woods, Ross; Clark, Martyn; Hamlet, Alan

2013-04-01

Increased access to water is a key pillar of the New Zealand government plan for economic growths. Variable climatic conditions coupled with market drivers and increased demand on water resource result in critical decision made by water managers based on climate and streamflow forecast. Because many of these decisions have serious economic implications, accurate forecast of climate and streamflow are of paramount importance (eg irrigated agriculture and electricity generation). New Zealand currently does not have a centralized, comprehensive, and state-of-the-art system in place for providing operational seasonal to interannual streamflow forecasts to guide water resources management decisions. As a pilot effort, we implement and evaluate an experimental ensemble streamflow forecasting system for the Waitaki and Rangitata River basins on New Zealand's South Island using a hydrologic simulation model (TopNet) and the familiar ensemble streamflow prediction (ESP) paradigm for estimating forecast uncertainty. To provide a comprehensive database for evaluation of the forecasting system, first a set of retrospective model states simulated by the hydrologic model on the first day of each month were archived from 1972-2009. Then, using the hydrologic simulation model, each of these historical model states was paired with the retrospective temperature and precipitation time series from each historical water year to create a database of retrospective hindcasts. Using the resulting database, the relative importance of initial state variables (such as soil moisture and snowpack) as fundamental drivers of uncertainties in forecasts were evaluated for different seasons and lead times. The analysis indicate that the sensitivity of flow forecast to initial condition uncertainty is depend on the hydrological regime and season of forecast. However initial conditions do not have a large impact on seasonal flow uncertainties for snow dominated catchments. Further analysis indicates that this result is valid when the hindcast database is conditioned by ENSO classification. As a result hydrological forecasts based on ESP technique, where present initial conditions with histological forcing data are used may be plausible for New Zealand catchments.

Forecasting new product diffusion using both patent citation and web search traffic.

PubMed

Lee, Won Sang; Choi, Hyo Shin; Sohn, So Young

2018-01-01

Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product's diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods.

Aggregate modeling of fast-acting demand response and control under real-time pricing

DOE PAGES

Chassin, David P.; Rondeau, Daniel

2016-08-24

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop amore » more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. Finally, the results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.« less

Aggregate modeling of fast-acting demand response and control under real-time pricing

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

Chassin, David P.; Rondeau, Daniel

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop amore » more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. Finally, the results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.« less

Aggregate modeling of fast-acting demand response and control under real-time pricing

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

Chassin, David P.; Rondeau, Daniel

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop amore » more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. The results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.« less

Forecasting Social Unrest Using Activity Cascades

PubMed Central

Cadena, Jose; Korkmaz, Gizem; Kuhlman, Chris J.; Marathe, Achla; Ramakrishnan, Naren; Vullikanti, Anil

2015-01-01

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen “on the ground.” Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach. PMID:26091012

Forecasting Social Unrest Using Activity Cascades.

PubMed

Cadena, Jose; Korkmaz, Gizem; Kuhlman, Chris J; Marathe, Achla; Ramakrishnan, Naren; Vullikanti, Anil

2015-01-01

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen "on the ground." Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.

Trends of jet fuel demand and properties

NASA Technical Reports Server (NTRS)

Friedman, R.

1984-01-01

Petroleum industry forecasts predict an increasing demand for jet fuels, a decrease in the gasoline-to-distillate (heavier fuel) demand ratio, and a greater influx of poorer quality petroleum in the next two to three decades. These projections are important for refinery product analyses. The forecasts have not been accurate, however, in predicting the recent, short term fluctuations in jet fuel and competing product demand. Changes in petroleum quality can be assessed, in part, by a review of jet fuel property inspections. Surveys covering the last 10 years show that average jet fuel freezing points, aromatic contents, and smoke points have trends toward their specification limits.

Examining lag effects between industrial land development and regional economic changes: The Netherlands experience.

PubMed

Ustaoglu, Eda; Lavalle, Carlo

2017-01-01

In most empirical applications, forecasting models for the analysis of industrial land focus on the relationship between current values of economic parameters and industrial land use. This paper aims to test this assumption by focusing on the dynamic relationship between current and lagged values of the 'economic fundamentals' and industrial land development. Not much effort has yet been attributed to develop land forecasting models to predict the demand for industrial land except those applying static regressions or other statistical measures. In this research, we estimated a dynamic panel data model across 40 regions from 2000 to 2008 for the Netherlands to uncover the relationship between current and lagged values of economic parameters and industrial land development. Land-use regulations such as land zoning policies, and other land-use restrictions like natural protection areas, geographical limitations in the form of water bodies or sludge areas are expected to affect supply of land, which will in turn be reflected in industrial land market outcomes. Our results suggest that gross domestic product (GDP), industrial employment, gross value added (GVA), property price, and other parameters representing demand and supply conditions in the industrial market explain industrial land developments with high significance levels. It is also shown that contrary to the current values, lagged values of the economic parameters have more sound relationships with the industrial developments in the Netherlands. The findings suggest use of lags between selected economic parameters and industrial land use in land forecasting applications.

Examining lag effects between industrial land development and regional economic changes: The Netherlands experience

PubMed Central

Ustaoglu, Eda; Lavalle, Carlo

2017-01-01

In most empirical applications, forecasting models for the analysis of industrial land focus on the relationship between current values of economic parameters and industrial land use. This paper aims to test this assumption by focusing on the dynamic relationship between current and lagged values of the ‘economic fundamentals’ and industrial land development. Not much effort has yet been attributed to develop land forecasting models to predict the demand for industrial land except those applying static regressions or other statistical measures. In this research, we estimated a dynamic panel data model across 40 regions from 2000 to 2008 for the Netherlands to uncover the relationship between current and lagged values of economic parameters and industrial land development. Land-use regulations such as land zoning policies, and other land-use restrictions like natural protection areas, geographical limitations in the form of water bodies or sludge areas are expected to affect supply of land, which will in turn be reflected in industrial land market outcomes. Our results suggest that gross domestic product (GDP), industrial employment, gross value added (GVA), property price, and other parameters representing demand and supply conditions in the industrial market explain industrial land developments with high significance levels. It is also shown that contrary to the current values, lagged values of the economic parameters have more sound relationships with the industrial developments in the Netherlands. The findings suggest use of lags between selected economic parameters and industrial land use in land forecasting applications. PMID:28877204

A multiscale forecasting method for power plant fleet management

NASA Astrophysics Data System (ADS)

Chen, Hongmei

In recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for high reliability. Therefore, to achieve true system excellence, a more sophisticated system-level decision-making process with a more accurate forecasting support system to manage diverse and often widely dispersed generation units as a single, easily scaled and deployed fleet system in order to fully utilize the critical assets of a power producer has been created as a response. The process takes into account the time horizon for each of the major decision actions taken in a power plant and develops methods for information sharing between them. These decisions are highly interrelated and no optimal operation can be achieved without sharing information in the overall process. The process includes a forecasting system to provide information for planning for uncertainty. A new forecasting method is proposed, which utilizes a synergy of several modeling techniques properly combined at different time-scales of the forecasting objects. It can not only take advantages of the abundant historical data but also take into account the impact of pertinent driving forces from the external business environment to achieve more accurate forecasting results. Then block bootstrap is utilized to measure the bias in the estimate of the expected life cycle cost which will actually be needed to drive the business for a power plant in the long run. Finally, scenario analysis is used to provide a composite picture of future developments for decision making or strategic planning. The decision-making process is applied to a typical power producer chosen to represent challenging customer demand during high-demand periods. The process enhances system excellence by providing more accurate market information, evaluating the impact of external business environment, and considering cross-scale interactions between decision actions. Along with this process, system operation strategies, maintenance schedules, and capacity expansion plans that guide the operation of the power plant are optimally identified, and the total life cycle costs are estimated.

Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

PubMed Central

Juang, Wang-Chuan; Huang, Sin-Jhih; Huang, Fong-Dee; Cheng, Pei-Wen; Wann, Shue-Ren

2017-01-01

Objective Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. Methods We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses. Results A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visitt=7111.161+(at+0.37462 at−1). Conclusion The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes. PMID:29196487

[A study on dental manpower distribution in Shanghai Pudong new district].

PubMed

Gu, Qin; Feng, Xi-ping

2006-02-01

A study of dental manpower distribution was made in Shanghai Pudong new district in order to analyze the needs and demands for dental services in Shanghai Pudong new district, to forecast the developmental trends of dental demand in the future and to provide basis for regional programs of dental manpower in the urban areas of China. An analysis was made in 601 subjects taken from all age groups in Shanghai Pudong new district by stratified and cluster random sampling and in 83 medical institutions of stomatology in Shanghai Pudong new district by mass examination. The amount of dental manpower need and demand was computed and forecasted by means of health care need and demand and proportional analogy methods. The total amounts needed were 755-834 dentists. The total amounts demanded were 285-314 dentists. It was forecasted that the figures would be 392-1041 in the year of 2010. The prevalence of oral disease was 90.18%, but only 37.66% of subjects visited dentist in a year. The ratio of dentists to the population was 1:9375. The unbalance between demand for and supply of dental manpower was mainly due to negative awareness of people, the irrationalness of demand levels, problems from service provider and the irrationalness of dental manpower levels.

Demand Forecasting: DLA’S Aviation Supply Chain High Value Products

DTIC Science & Technology

2015-04-09

program at USS CONSTELLATION (CV 64), San Diego CA LCDR Carlos Lopez Education  MBA in Supply Chain Management, Naval Postgraduate School  BS in...Exponential Smoothing Forecasts ............... 118 xv Figure 80. NIIN 01-463-4340 Seasonal Exponential Smoothing Forecast .............. 119 Figure...5310 Seasonal Exponential Smoothing ............................ 142 Figure 102. NIIN 01-507-5310 12-Month Forecast Simulation

Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas

NASA Astrophysics Data System (ADS)

Uddameri, V.

2007-01-01

Reliable forecasts of monthly and quarterly fluctuations in groundwater levels are necessary for short- and medium-term planning and management of aquifers to ensure proper service of seasonal demands within a region. Development of physically based transient mathematical models at this time scale poses considerable challenges due to lack of suitable data and other uncertainties. Artificial neural networks (ANN) possess flexible mathematical structures and are capable of mapping highly nonlinear relationships. Feed-forward neural network models were constructed and trained using the back-percolation algorithm to forecast monthly and quarterly time-series water levels at a well that taps into the deeper Evangeline formation of the Gulf Coast aquifer in Victoria, TX. Unlike unconfined formations, no causal relationships exist between water levels and hydro-meteorological variables measured near the vicinity of the well. As such, an endogenous forecasting model using dummy variables to capture short-term seasonal fluctuations and longer-term (decadal) trends was constructed. The root mean square error, mean absolute deviation and correlation coefficient ( R) were noted to be 1.40, 0.33 and 0.77 m, respectively, for an evaluation dataset of quarterly measurements and 1.17, 0.46, and 0.88 m for an evaluative monthly dataset not used to train or test the model. These statistics were better for the ANN model than those developed using statistical regression techniques.

Agent Based Model of Livestock Movements

NASA Astrophysics Data System (ADS)

Miron, D. J.; Emelyanova, I. V.; Donald, G. E.; Garner, G. M.

The modelling of livestock movements within Australia is of national importance for the purposes of the management and control of exotic disease spread, infrastructure development and the economic forecasting of livestock markets. In this paper an agent based model for the forecasting of livestock movements is presented. This models livestock movements from farm to farm through a saleyard. The decision of farmers to sell or buy cattle is often complex and involves many factors such as climate forecast, commodity prices, the type of farm enterprise, the number of animals available and associated off-shore effects. In this model the farm agent's intelligence is implemented using a fuzzy decision tree that utilises two of these factors. These two factors are the livestock price fetched at the last sale and the number of stock on the farm. On each iteration of the model farms choose either to buy, sell or abstain from the market thus creating an artificial supply and demand. The buyers and sellers then congregate at the saleyard where livestock are auctioned using a second price sealed bid. The price time series output by the model exhibits properties similar to those found in real livestock markets.

Nested hyper-resolution modeling of urban areas for the National Water Model - The Dallas-Fort Worth Testbed

NASA Astrophysics Data System (ADS)

Noh, S. J.; Kim, S.; Habibi, H.; Seo, D. J.; Welles, E.; Philips, B.; Adams, E.; Smith, M. B.; Wells, E.

2017-12-01

With the development of the National Water Model (NWM), the NWS has made a step-change advance in operational water forecasting by enabling high-resolution hydrologic modeling across the US. As a part of a separate initiative to enhance flash flood forecasting and inundation mapping capacity, the NWS has been mandated to provide forecasts at even finer spatiotemporal resolutions when and where such information is demanded. In this presentation, we describe implementation of the NWM at a hyper resolution over a nested domain. We use WRF-Hydro as the core model but at significantly higher resolutions with scale-commensurate model parameters. The demonstration domain is multiple urban catchments within the Cities of Arlington and Grand Prairie in the Dallas-Fort Worth Metroplex. This area is susceptible to urban flooding due to the hydroclimatology coupled with large impervious cover. The nested model is based on hyper-resolution terrain data to resolve significant land surface features such as streets and large man-made structures, and forced by the high-resolution radar-based quantitative precipitation information. In this presentation, we summarize progress and preliminary results and share issues and challenges.

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.

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.

FP7 GLOWASIS - A new collaborative project aimed at pre-validation of a GMES Global Water Scarcity Information Service

NASA Astrophysics Data System (ADS)

Westerhoff, R.; Levizzani, V.; Pappenberger, F.; de Roo, A.; Lange, R. D.; Wagner, W.; Bierkens, M. F.; Ceran, M.; Weerts, A.; Sinclair, S.; Miguez-Macho, G.; Langius, E.; Glowasis Team

2011-12-01

The main objective of the project GLOWASIS is to pre-validate a GMES Global Service for Water Scarcity Information. It will be set up as a one-stop-shop portal for water scarcity information, in which focus is put on: - monitoring data from satellites and in-situ sensors; - improving forecasting models with improved monitoring data; - linking statistical water data in forecasting; - promotion of GMES Services and European satellites. In European and global pilots on the scale of river catchments it combines hydrological models with in-situ and satellite derived water cycle information, as well as government ruled statistical water demand data. By linking water demand and supply in three pilot studies with existing platforms (European Drought Observatory and PCR-GLOBWB) for medium- and long-term forecasting in Europe, Africa and worldwide, GLOWASIS' information contributes both in near-real time reporting for emerging drought events as well as in provision of climate change time series. By combining complex water cycle variables, governmental issues and economic relations with respect to water demand, GLOWASIS will aim for the needed streamlining of the wide variety of important water scarcity information. More awareness for the complexity of the water scarcity problem will be created and additional capabilities of satellite-measured water cycle parameters can be promoted. The service uses data from GMES Core Services LMCS Geoland2 and Marine Core Service MyOcean (land use, soil moisture, soil sealing, sea level), in-situ data from GEWEX' initiatives (i.e. International Soil Moisture network), agricultural and industrial water use and demand (statistical - AQUASTAT, SEEAW and modelled) and additional water-cycle information from existing global satellite services. In-depth interviews with a.o. EEA and the Australian Bureau of Meteorology are taking place. GLOWASIS will aim for an open source and open-standard information portal on water scarcity and use of modern media (forums, Twitter, etc). Infrastructure of the GLOWASIS portal is set up for dissemination and inclusion of current and future innovative and integrated multi-purpose products for research & operational applications with open standards. The project has started in January 2011 and the duration is 24 months.

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

Transportation Energy Futures Series: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future

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

Grenzeback, L. R.; Brown, A.; Fischer, M. J.

2013-03-01

Freight transportation demand is projected to grow to 27.5 billion tons in 2040, and to nearly 30.2 billion tons in 2050. This report describes the current and future demand for freight transportation in terms of tons and ton-miles of commodities moved by truck, rail, water, pipeline, and air freight carriers. It outlines the economic, logistics, transportation, and policy and regulatory factors that shape freight demand, the trends and 2050 outlook for these factors, and their anticipated effect on freight demand. After describing federal policy actions that could influence future freight demand, the report then summarizes the capabilities of available analyticalmore » models for forecasting freight demand. This is one in a series of reports produced as a result of the Transportation Energy Futures project, a Department of Energy-sponsored multi-agency effort to pinpoint underexplored strategies for reducing GHGs and petroleum dependence related to transportation.« less

Advanced inflow forecasting for a hydropower plant in an Alpine hydropower regulated catchment - coupling of operational and hydrological forecasts

NASA Astrophysics Data System (ADS)

Tilg, Anna-Maria; Schöber, Johannes; Huttenlau, Matthias; Messner, Jakob; Achleitner, Stefan

2017-04-01

Hydropower is a renewable energy source which can help to stabilize fluctuations in the volatile energy market. Especially pumped-storage infrastructures in the European Alps play an important role within the European energy grid system. Today, the runoff of rivers in the Alps is often influenced by cascades of hydropower infrastructures where the operational procedures are triggered by energy market demands, water deliveries and flood control aspects rather than by hydro-meteorological variables. An example for such a highly hydropower regulated river is the catchment of the river Inn in the Eastern European Alps, originating in the Engadin (Switzerland). A new hydropower plant is going to be built as transboundary project at the boarder of Switzerland and Austria using the water of the Inn River. For the operation, a runoff forecast to the plant is required. The challenge in this case is that a high proportion of runoff is turbine water from an upstream situated hydropower cascade. The newly developed physically based hydrological forecasting system is mainly capable to cover natural hydrological runoff processes caused by storms and snow melt but can model only a small degree of human impact. These discontinuous parts of the runoff downstream of the pumped storage are described by means of an additional statistical model which has been developed. The main goal of the statistical model is to forecast the turbine water up to five days in advance. The lead time of the data driven model exceeds the lead time of the used energy production forecast. Additionally, the amount of turbine water is linked to the need of electricity production and the electricity price. It has been shown that especially the parameters day-ahead prognosis of the energy production and turbine inflow of the previous week are good predictors and are therefore used as input parameters for the model. As the data is restricted due to technical conditions, so-called Tobit models have been used to develop a linear regression for the runoff forecast. Although the day-ahead prognosis cannot always be kept, the regression model delivers, especially during office hours, very reasonable results. In the remaining hours the error between measurement and the forecast increases. Overall, the inflow forecast can be substantially improved by the implementation of the developed regression in the hydrological modelling system.

Season-ahead streamflow forecast informed tax strategies for semi-arid water rights markets

NASA Astrophysics Data System (ADS)

Delorit, J. D.; Block, P. J.

2016-12-01

In many semi-arid regions multisectoral demands stress available water supplies. The Elqui River valley of north central Chile, which draws on limited capacity reservoirs supplied largely by annually variable snowmelt, is one of these cases. This variability forces water managers to develop demand-based allocation strategies which have typically resulted in water right volume reductions, applied equally per right. Compounding this issue is often deferred or delayed infrastructure investments, which has been linked Chile's Coasian approach to water markets, under which rights holders do not pay direct procurement costs, non-use fees, nor taxes. Here we build upon our previous research using forecasts of likely water rights reductions, informed by season-ahead prediction models of October-January (austral growing season) streamflow, to construct annual, forecast-sensitive, per right tax. We believe this tax, to be borne by right holders, will improve the beneficial use of water resources by stimulating water rights trading and improving system efficiency by generating funds for infrastructure investment, thereby reducing free-ridership and conflict between rights holders. Research outputs will include sectoral per right tax assessments, tax revenue generation, Elqui River valley economic output, and water rights trading activity.

System for NIS Forecasting Based on Ensembles Analysis

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

2014-01-02

BMA-NIS is a package/library designed to be called by a script (e.g. Perl or Python). The software itself is written in the language of R. The software assists electric power delivery systems in planning resource availability and demand, based on historical data and current data variables. Net Interchange Schedule (NIS) is the algebraic sum of all energy scheduled to flow into or out of a balancing area during any interval. Accurate forecasts for NIS are important so that the Area Control Error (ACE) stays within an acceptable limit. To date, there are many approaches for forecasting NIS but all nonemore » of these are based on single models that can be sensitive to time of day and day of week effects.« less

Grand European and Asian-Pacific multi-model seasonal forecasts: maximization of skill and of potential economical value to end-users

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Felice, Matteo De; Catalano, Franco; Lee, June-Yi; Wang, Bin; Lee, Doo Young; Yoo, Jin-Ho; Weisheimer, Antije

2018-04-01

Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles. Previous works suggested that the potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two MME Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (APCC/CliPAS) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from APCC/CliPAS have been evaluated. The grand ENSEMBLES-APCC/CliPAS MME enhances significantly the skill in predicting 2m temperature and precipitation compared to previous estimates from the contributing MMEs. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and APCC/CliPAS models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration so that all models are useful in some cases. It is shown that the incremental performance contribution tends to be higher when adding one model from ENSEMBLES to APCC/CliPAS MMEs and vice versa, confirming that the benefit of using MMEs amplifies with the increase of the independence the contributing models. To verify the above results for a real world application, the Grand ENSEMBLES-APCC/CliPAS MME is used to predict retrospective energy demand over Italy as provided by TERNA (Italian Transmission System Operator) for the period 1990-2007. The results demonstrate the useful application of MME seasonal predictions for energy demand forecasting over Italy. It is shown a significant enhancement of the potential economic value of forecasting energy demand when using the better combinations from the Grand MME by comparison to the maximum value obtained from the better combinations of each of the two contributing MMEs. The above results demonstrate for the first time the potential of the Grand MME to significantly contribute in obtaining useful predictions at the seasonal time-scale.

Will the NP workforce grow in the future? New forecasts and implications for healthcare delivery.

PubMed

Auerbach, David I

2012-07-01

The nurse practitioner (NP) workforce has been a focus of considerable policy interest recently, particularly as the Patient Protection and Affordable Care Act may place additional demands on the healthcare professional workforce. The NP workforce has been growing rapidly in recent years, but fluctuation in enrollments in the past decades has resulted in a wide range of forecasts. To forecast the future NP workforce using a novel method that has been applied to the registered nurse and physician workforces and is robust to fluctuating enrollment trends. An age-cohort regression-based model was applied to the current and historical workforce, which was then forecasted to future years assuming stable age effects and a continuation of recent cohort trends. A total of 6798 NPs who were identified as having completed NP training in the National Sample Survey of Registered Nurses between 1992 and 2008. The future workforce is projected to grow to 244,000 in 2025, an increase of 94% from 128,000 in 2008. If NPs are defined more restrictively as those who self-identify their position title as "NP," supply is projected to grow from 86,000 to 198,000 (130%) over this period. The large projected increase in NP supply is higher and more grounded than other forecasts and has several implications: NPs will likely fulfill a substantial amount of future demand for care. Furthermore, as the ratio of NPs to Nurse Practitioners to physicians will surely grow, there could be implications for quality of care and for the configuration of future care delivery systems.

A Bayesian approach to multisource forest area estimation

Treesearch

Andrew O. Finley

2007-01-01

In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, demand is increasing for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest and, most importantly, provide associated error estimates. This paper presents a model-based approach for...

Hawaii energy strategy report, October 1995

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

NONE

This is a report on the Hawaii Energy Strategy Program. The topics of the report include the a description of the program including an overview, objectives, policy statement and purpose and objectives; energy strategy policy development; energy strategy projects; current energy situation; modeling Hawaii`s energy future; energy forecasts; reducing energy demand; scenario assessment, and recommendations.

Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations

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

Chen, Qifang; Wang, Fei; Hodge, Bri-Mathias

A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and uppermore » bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.« less

Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.

PubMed

Ouyang, Yicun; Yin, Hujun

2018-05-01

Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.

Grand European and Asian-Pacific multi-model seasonal forecasts: maximization of skill and of potential economical value to end-users

NASA Astrophysics Data System (ADS)

Alessandri, A.; De Felice, M.; Catalano, F.; Lee, J. Y.; Wang, B.; Lee, D. Y.; Yoo, J. H.; Weisheimer, A.

2017-12-01

By initiating a novel cooperation between the European and the Asian-Pacific climate-prediction communities, this work demonstrates the potential of gathering together their Multi-Model Ensembles (MMEs) to obtain useful climate predictions at seasonal time-scale.MMEs are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles and increasing benefit is expected with the increase of the independence of the contributing Seasonal Prediction Systems (SPSs). In this work we combine the two MME SPSs independently developed by the European (ENSEMBLES) and by the Asian-Pacific (APCC/CliPAS) communities by establishing an unprecedented partnerships. To this aim, all the possible MME combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from APCC/CliPAS have been evaluated. The Grand ENSEMBLES-APCC/CliPAS MME enhances significantly the skill in predicting 2m temperature and precipitation. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and APCC/CliPAS models and that only a limited number of SPSs is required to obtain the maximum performance. The selection of models that perform better is usually different depending on the region/phenomenon under consideration so that all models are useful in some cases. It is shown that the incremental performance contribution tends to be higher when adding one model from ENSEMBLES to APCC/CliPAS MMEs and vice versa, confirming that the benefit of using MMEs amplifies with the increase of the independence the contributing models.To verify the above results for a real world application, the Grand MME is used to predict energy demand over Italy as provided by TERNA (Italian Transmission System Operator) for the period 1990-2007. The results demonstrate the useful application of MME seasonal predictions for energy demand forecasting over Italy. It is shown a significant enhancement of the potential economic value of forecasting energy demand when using the better combinations from the Grand MME by comparison to the maximum value obtained from the better combinations of each of the two contributing MMEs. Above results are discussed in a Clim Dyn paper (Alessandri et al., 2017; doi:10.1007/s00382-016-3372-4).

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

NASA Astrophysics Data System (ADS)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

Market-based demand forecasting promotes informed strategic financial planning.

PubMed

Beech, A J

2001-11-01

Market-based demand forecasting is a method of estimating future demand for a healthcare organization's services by using a broad range of data that describe the nature of demand within the organization's service area. Such data include the primary and secondary service areas, the service-area populations by various demographic groupings, discharge utilization rates, market size, and market share by service line and organizationwide. Based on observable market dynamics, strategic planners can make a variety of explicit assumptions about future trends regarding these data to develop scenarios describing potential future demand. Financial planners then can evaluate each scenario to determine its potential effect on selected financial and operational measures, such as operating margin, days cash on hand, and debt-service coverage, and develop a strategic financial plan that covers a range of contingencies.

Confronting the demand and supply of snow seasonal forecasts for ski resorts : the case of French Alps

NASA Astrophysics Data System (ADS)

Dubois, Ghislain

2017-04-01

Alpine ski resorts are highly dependent on snow, which availability is characterized by a both a high inter-annual variability and a gradual diminution due to climate change. Due to this dependency to climatic resources, the ski industry is increasingly affected by climate change: higher temperatures limit snow falls, increase melting and limit the possibilities of technical snow making. Therefore, since the seventies, managers drastically improved their practices, both to adapt to climate change and to this inter-annual variability of snow conditions. Through slope preparation and maintenance, snow stock management, artificial snow making, a typical resort can approximately keep the same season duration with 30% less snow. The ski industry became an activity of high technicity The EUPORIAS FP7 (www.euporias.eu) project developed between 2012 and 2016 a deep understanding of the supply and demand conditions for the provision of climate services disseminating seasonal forecasts. In particular, we developed a case study, which allowed conducting several activities for a better understanding of the demand and of the business model of future services applied to the ski industry. The investigations conducted in France inventoried the existing tools and databases, assessed the decision making process and data needs of ski operators, and provided evidences that some discernable skill of seasonal forecasts exist. This case study formed the basis of the recently funded PROSNOW H2020 project. We will present the main results of EUPORIAS project for the ski industry.

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...

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.

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.

Using NMME in Region-Specific Operational Seasonal Climate Forecasts

NASA Astrophysics Data System (ADS)

Gronewold, A.; Bolinger, R. A.; Fry, L. M.; Kompoltowicz, K.

2015-12-01

The National Oceanic and Atmospheric Administration's Climate Prediction Center (NOAA/CPC) provides access to a suite of real-time monthly climate forecasts that comprise the North American Multi-Model Ensemble (NMME) in an attempt to meet increasing demands for monthly to seasonal climate prediction. While the graphical map forecasts of the NMME are informative, there is a need to provide decision-makers with probabilistic forecasts specific to their region of interest. Here, we demonstrate the potential application of the NMME to address regional climate projection needs by developing new forecasts of temperature and precipitation for the North American Great Lakes, the largest system of lakes on Earth. Regional opertional water budget forecasts rely on these outlooks to initiate monthly forecasts not only of the water budget, but of monthly lake water levels as well. More specifically, we present an alternative for improving existing operational protocols that currently involve a relatively time-consuming and subjective procedure based on interpreting the maps of the NMME. In addition, all forecasts are currently presented in the NMME in a probabilistic format, with equal weighting given to each member of the ensemble. In our new evolution of this product, we provide historical context for the forecasts by superimposing them (in an on-line graphical user interface) with the historical range of observations. Implementation of this new tool has already led to noticeable advantages in regional water budget forecasting, and has the potential to be transferred to other regional decision-making authorities as well.

The Economic Effects of Reduced Defense Spending

DTIC Science & Technology

1992-02-01

Macroeconomics Annual 1987 (Cambridge, Mass.: MIT Press), pp. 263-315; and Leonardo Leiderman and Mario Blejer, " Modeling and Testing Ricardian ... model ), converting LIFT’s 78-sector forecasts of final demand by product to estimates for some 420 producing sectors. Unlike LIFT, DOM does not...24s in production in Fort Worth, Texas, during World War II. Photo is courtesy of Gen- eral Dynamics Corporation. Preface The past three years

Forecasting Ontario's blood supply and demand.

PubMed

Drackley, Adam; Newbold, K Bruce; Paez, Antonio; Heddle, Nancy

2012-02-01

Given an aging population that requires increased medical care, an increasing number of deferrals from the donor pool, and a growing immigrant population that typically has lower donation rates, the purpose of this article is to forecast Ontario's blood supply and demand. We calculate age- and sex-specific donation and demand rates for blood supply based on 2008 data and project demand between 2008 and 2036 based on these rates and using population data from the Ontario Ministry of Finance. Results indicate that blood demand will outpace supply as early as 2012. For instance, while the total number of donations made by older cohorts is expected to increase in the coming years, the number of red blood cell (RBC) transfusions in the 70+ age group is forecasted grow from approximately 53% of all RBC transfusions in 2008 (209,515) in 2008 to 68% (546,996) by 2036. A series of alternate scenarios, including projections based on a 2% increase in supply per year and increased use of apheresis technology, delays supply shortfalls, but does not eliminate them without active management and/or multiple methods to increase supply and decrease demand. Predictions show that demand for blood products will outpace supply in the near future given current age- and sex-specific supply and demand rates. However, we note that the careful management of the blood supply by Canadian Blood Services, along with new medical techniques and the recruitment of new donors to the system, will remove future concerns. © 2012 American Association of Blood Banks.

Climate, weather, space weather: model development in an operational context

NASA Astrophysics Data System (ADS)

Folini, Doris

2018-05-01

Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of "operational stability" versus "dynamic development" of the involved models. Some common key elements are identified, indicating potential for fruitful exchange across communities. Operational model development is compelling, driven by factors that broadly fall into four categories: model skill, basic physics, advances in computer architecture, and new aspects to be covered, from costumer needs over physics to observational data. Evaluation of model skill as part of the operational chain goes beyond an automated skill score. Permanent interaction between "pure research" and "operational forecast" people is beneficial to both sides. This includes joint model development projects, although ultimate responsibility for the operational code remains with the forecast provider. The pace of model development reflects operational lead times. The points are illustrated with selected examples, many of which reflect the author's background and personal contacts, notably with the Swiss Weather Service and the Max Planck Institute for Meteorology, Hamburg, Germany. In view of current and future challenges, large collaborations covering a range of expertise are a must - within and across climate, weather, and space weather. To profit from and cope with the rapid progress of computer architectures, supercompute centers must form part of the team.

"DEP'ART." Un Modele de Prevision des Departs des Enseignants. Documents Demographie Scolaire 9-16 ("DEP'ART." A Model for Predicting Teacher Attrition. Scholastic Demographic Document 9-16).

ERIC Educational Resources Information Center

Meublat, Guy

This document forms part of a research project initiated by the Ministry of Education in Quebec and designed to forecast teacher demand over the next 15 years. It analyzes the problem of identifying potential teacher dropouts by means of a statistical model which provides simulations of various hypotheses and which can be easily revised by the…

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.

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.

Forecasting the shortage of neurosurgeons in Iran using a system dynamics model approach.

PubMed

Rafiei, Sima; Daneshvaran, Arman; Abdollahzade, Sina

2018-01-01

Shortage of physicians particularly in specialty levels is considered as an important issue in Iran health system. Thus, in an uncertain environment, long-term planning is required for health professionals as a basic priority on a national scale. This study aimed to estimate the number of required neurosurgeons using system dynamic modeling. System dynamic modeling was applied to predict the gap between stock and number of required neurosurgeons in Iran up to 2020. A supply and demand simulation model was constructed for neurosurgeons using system dynamic approach. The demand model included epidemiological, demographic, and utilization variables along with supply model-incorporated current stock of neurosurgeons and flow variables such as attrition, migration, and retirement rate. Data were obtained from various governmental databases and were analyzed by Vensim PLE Version 3.0 to address the flow of health professionals, clinical infrastructure, population demographics, and disease prevalence during the time. It was forecasted that shortage in number of neurosurgeons would disappear at 2020. The most dominant determinants on predicted number of neurosurgeons were the prevalence of neurosurgical diseases, the rate for service utilization, and medical capacity of the region. Shortage of neurosurgeons in some areas of the country relates to maldistribution of the specialists. Accordingly, there is a need to reconsider the allocation system for health professionals within the country instead of increasing the overall number of acceptance quota in training positions.

Long-term forecasting of internet backbone traffic.

PubMed

Papagiannaki, Konstantina; Taft, Nina; Zhang, Zhi-Li; Diot, Christophe

2005-09-01

We introduce a methodology to predict when and where link additions/upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent points of presence (PoPs) and look at its evolution at time scales larger than 1 h. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis (MRA) and linear time series models. Using wavelet MRA, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12-h time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12-h time scale yields accurate estimates for at least 6 months in the future.

Forecasting new product diffusion using both patent citation and web search traffic

PubMed Central

Lee, Won Sang; Choi, Hyo Shin

2018-01-01

Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product’s diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods. PMID:29630616

Outlook for Biomass Ethanol Production and Demand

EIA Publications

2000-01-01

This paper presents a midterm forecast for biomass ethanol production under three different technology cases for the period 2000 to 2020, based on projections developed from the Energy Information Administration's National Energy Modeling System. An overview of cellulose conversion technology and various feedstock options and a brief history of ethanol usage in the United States are also presented.

Hawaii energy strategy: Executive summary, October 1995

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

NONE

This is an executive summary to a report on the Hawaii Energy Strategy Program. The topics of the report include the a description of the program including an overview, objectives, policy statement and purpose and objectives; energy strategy policy development; energy strategy projects; current energy situation; modeling Hawaii`s energy future; energy forecasts; reducing energy demand; scenario assessment, and recommendations.

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.

Residential energy demand models: Current status and future improvements

NASA Astrophysics Data System (ADS)

Peabody, G.

1980-12-01

Two models currently used to analyze energy use by the residential sector are described. The ORNL model is used to forecast energy use by fuel type for various end uses on a yearly basis. The MATH/CHRDS model analyzes variations in energy expenditures by households of various socioeconomic and demographic characteristics. The essential features of the ORNL and MATH/CHRDS models are retained in a proposed model and integrated into a framework that is more flexible than either model. The important determinants of energy use by households are reviewed.

Climate Change Impacts on Worldwide Coffee Production

NASA Astrophysics Data System (ADS)

Foreman, T.; Rising, J. A.

2015-12-01

Coffee (Coffea arabica and Coffea canephora) plays a vital role in many countries' economies, providing necessary income to 25 million members of tropical countries, and supporting a $81 billion industry, making it one of the most valuable commodities in the world. At the same time, coffee is at the center of many issues of sustainability. It is vulnerable to climate change, with disease outbreaks becoming more common and suitable regions beginning to shift. We develop a statistical production model for coffee which incorporates temperature, precipitation, frost, and humidity effects using a new database of worldwide coffee production. We then use this model to project coffee yields and production into the future based on a variety of climate forecasts. This model can then be used together with a market model to forecast the locations of future coffee production as well as future prices, supply, and demand.

Improving Energy Use Forecast for Campus Micro-grids using Indirect Indicators

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

Aman, Saima; Simmhan, Yogesh; Prasanna, Viktor K.

2011-12-11

The rising global demand for energy is best addressed by adopting and promoting sustainable methods of power consumption. We employ an informatics approach towards forecasting the energy consumption patterns in a university campus micro-grid which can be used for energy use planning and conservation. We use novel indirect indicators of energy that are commonly available to train regression tree models that can predict campus and building energy use for coarse (daily) and fine (15-min) time intervals, utilizing 3 years of sensor data collected at 15min intervals from 170 smart power meters. We analyze the impact of individual features used inmore » the models to identify the ones best suited for the application. Our models show a high degree of accuracy with CV-RMSE errors ranging from 7.45% to 19.32%, and a reduction in error from baseline models by up to 53%.« less

Prediction of Weather Impacted Airport Capacity using Ensemble Learning

NASA Technical Reports Server (NTRS)

Wang, Yao Xun

2011-01-01

Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.

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.

Forecasting paratransit services demand : review and recommendations - [summary].

DOT National Transportation Integrated Search

2013-01-01

In 2012, the Government Accounting Office reported increasing demand for paratransit services, public transit for those unable to operate a motor vehicle. In Florida, this demand is based on a growing number of people with disabilities or low incomes...

Seasonal streamflow prediction using ensemble streamflow prediction technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

NASA Astrophysics Data System (ADS)

Singh, Shailesh Kumar

2014-05-01

Streamflow forecasts are essential for making critical decision for optimal allocation of water supplies for various demands that include irrigation for agriculture, habitat for fisheries, hydropower production and flood warning. The major objective of this study is to explore the Ensemble Streamflow Prediction (ESP) based forecast in New Zealand catchments and to highlights the present capability of seasonal flow forecasting of National Institute of Water and Atmospheric Research (NIWA). In this study a probabilistic forecast framework for ESP is presented. The basic assumption in ESP is that future weather pattern were experienced historically. Hence, past forcing data can be used with current initial condition to generate an ensemble of prediction. Small differences in initial conditions can result in large difference in the forecast. The initial state of catchment can be obtained by continuously running the model till current time and use this initial state with past forcing data to generate ensemble of flow for future. The approach taken here is to run TopNet hydrological models with a range of past forcing data (precipitation, temperature etc.) with current initial conditions. The collection of runs is called the ensemble. ESP give probabilistic forecasts for flow. From ensemble members the probability distributions can be derived. The probability distributions capture part of the intrinsic uncertainty in weather or climate. An ensemble stream flow prediction which provide probabilistic hydrological forecast with lead time up to 3 months is presented for Rangitata, Ahuriri, and Hooker and Jollie rivers in South Island of New Zealand. ESP based seasonal forecast have better skill than climatology. This system can provide better over all information for holistic water resource management.

Forty and 80 GHz technology assessment and forecast including executive summary

NASA Technical Reports Server (NTRS)

Mazur, D. G.; Mackey, R. J., Jr.; Tanner, S. G.; Altman, F. J.; Nicholas, J. J., Jr.; Duchaine, K. A.

1976-01-01

The results of a survey to determine current demand and to forecast growth in demand for use of the 40 and 80 GHz bands during the 1980-2000 time period are given. The current state-of-the-art is presented, as well as the technology requirements of current and projected services. Potential developments were identified, and a forecast is made. The impacts of atmospheric attenuation in the 40 and 80 GHz bands were estimated for both with and without diversity. Three services for the 1980-2000 time period -- interactive television, high quality three stereo pair audio, and 30 MB data -- are given with system requirements and up and down-link calculations.

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.

Evaluating sub-seasonal skill in probabilistic forecasts of Atmospheric Rivers and associated extreme events

NASA Astrophysics Data System (ADS)

Subramanian, A. C.; Lavers, D.; Matsueda, M.; Shukla, S.; Cayan, D. R.; Ralph, M.

2017-12-01

Atmospheric rivers (ARs) - elongated plumes of intense moisture transport - are a primary source of hydrological extremes, water resources and impactful weather along the West Coast of North America and Europe. There is strong demand in the water management, societal infrastructure and humanitarian sectors for reliable sub-seasonal forecasts, particularly of extreme events, such as floods and droughts so that actions to mitigate disastrous impacts can be taken with sufficient lead-time. Many recent studies have shown that ARs in the Pacific and the Atlantic are modulated by large-scale modes of climate variability. Leveraging the improved understanding of how these large-scale climate modes modulate the ARs in these two basins, we use the state-of-the-art multi-model forecast systems such as the North American Multi-Model Ensemble (NMME) and the Subseasonal-to-Seasonal (S2S) database to help inform and assess the probabilistic prediction of ARs and related extreme weather events over the North American and European West Coasts. We will present results from evaluating probabilistic forecasts of extreme precipitation and AR activity at the sub-seasonal scale. In particular, results from the comparison of two winters (2015-16 and 2016-17) will be shown, winters which defied canonical El Niño teleconnection patterns over North America and Europe. We further extend this study to analyze probabilistic forecast skill of AR events in these two basins and the variability in forecast skill during certain regimes of large-scale climate modes.

Forecast of long term coal supply and mining conditions: Model documentation and results

NASA Technical Reports Server (NTRS)

1980-01-01

A coal industry model was developed to support the Jet Propulsion Laboratory in its investigation of advanced underground coal extraction systems. The model documentation includes the programming for the coal mining cost models and an accompanying users' manual, and a guide to reading model output. The methodology used in assembling the transportation, demand, and coal reserve components of the model are also described. Results presented for 1986 and 2000, include projections of coal production patterns and marginal prices, differentiated by coal sulfur content.

The Use of Scale-Dependent Precision to Increase Forecast Accuracy in Earth System Modelling

NASA Astrophysics Data System (ADS)

Thornes, Tobias; Duben, Peter; Palmer, Tim

2016-04-01

At the current pace of development, it may be decades before the 'exa-scale' computers needed to resolve individual convective clouds in weather and climate models become available to forecasters, and such machines will incur very high power demands. But the resolution could be improved today by switching to more efficient, 'inexact' hardware with which variables can be represented in 'reduced precision'. Currently, all numbers in our models are represented as double-precision floating points - each requiring 64 bits of memory - to minimise rounding errors, regardless of spatial scale. Yet observational and modelling constraints mean that values of atmospheric variables are inevitably known less precisely on smaller scales, suggesting that this may be a waste of computer resources. More accurate forecasts might therefore be obtained by taking a scale-selective approach whereby the precision of variables is gradually decreased at smaller spatial scales to optimise the overall efficiency of the model. To study the effect of reducing precision to different levels on multiple spatial scales, we here introduce a new model atmosphere developed by extending the Lorenz '96 idealised system to encompass three tiers of variables - which represent large-, medium- and small-scale features - for the first time. In this chaotic but computationally tractable system, the 'true' state can be defined by explicitly resolving all three tiers. The abilities of low resolution (single-tier) double-precision models and similar-cost high resolution (two-tier) models in mixed-precision to produce accurate forecasts of this 'truth' are compared. The high resolution models outperform the low resolution ones even when small-scale variables are resolved in half-precision (16 bits). This suggests that using scale-dependent levels of precision in more complicated real-world Earth System models could allow forecasts to be made at higher resolution and with improved accuracy. If adopted, this new paradigm would represent a revolution in numerical modelling that could be of great benefit to the world.

The national operational environment model (NOEM)

NASA Astrophysics Data System (ADS)

Salerno, John J.; Romano, Brian; Geiler, Warren

2011-06-01

The National Operational Environment Model (NOEM) is a strategic analysis/assessment tool that provides insight into the complex state space (as a system) that is today's modern operational environment. The NOEM supports baseline forecasts by generating plausible futures based on the current state. It supports what-if analysis by forecasting ramifications of potential "Blue" actions on the environment. The NOEM also supports sensitivity analysis by identifying possible pressure (leverage) points in support of the Commander that resolves forecasted instabilities, and by ranking sensitivities in a list for each leverage point and response. The NOEM can be used to assist Decision Makers, Analysts and Researchers with understanding the inter-workings of a region or nation state, the consequences of implementing specific policies, and the ability to plug in new operational environment theories/models as they mature. The NOEM is built upon an open-source, license-free set of capabilities, and aims to provide support for pluggable modules that make up a given model. The NOEM currently has an extensive number of modules (e.g. economic, security & social well-being pieces such as critical infrastructure) completed along with a number of tools to exercise them. The focus this year is on modeling the social and behavioral aspects of a populace within their environment, primarily the formation of various interest groups, their beliefs, their requirements, their grievances, their affinities, and the likelihood of a wide range of their actions, depending on their perceived level of security and happiness. As such, several research efforts are currently underway to model human behavior from a group perspective, in the pursuit of eventual integration and balance of populace needs/demands within their respective operational environment and the capacity to meet those demands. In this paper we will provide an overview of the NOEM, the need for and a description of its main components. We will also provide a detailed discussion of the model and sample use cases.

The evaluation of the optimization design and application effect of same-well-injection-production technique’s injection-production circulatory system

NASA Astrophysics Data System (ADS)

Guoxing, Zheng; Minghu, Jiang; Hongliang, Gong; Nannan, Zhang; Jianguang, Wei

2018-02-01

According to basic principles of combining series of strata and demands of same-well injection-production technique, the optimization designing method of same-well injection-production technique’s injection-production circulatory system is given. Based on oil-water two-phase model with condition of arbitrarily well network, a dynamic forecast method for the application of same-well injection-production reservoir is established with considering the demands and capacity of same-well injection-production technique, sample wells are selected to launch the forecast evaluation and analysis of same-well injection-production reservoir application’s effect. Results show: single-test-well composite water cut decreases by 4.7% and test-well-group composite water cut decreases by 1.56% under the condition of basically invariant ground water injection rate. The method provides theoretical support for the proof of same-well injection-production technique’s reservoir development improving effect and further tests.

First Results of the Regional Earthquake Likelihood Models Experiment

USGS Publications Warehouse

Schorlemmer, D.; Zechar, J.D.; Werner, M.J.; Field, E.H.; Jackson, D.D.; Jordan, T.H.

2010-01-01

The ability to successfully predict the future behavior of a system is a strong indication that the system is well understood. Certainly many details of the earthquake system remain obscure, but several hypotheses related to earthquake occurrence and seismic hazard have been proffered, and predicting earthquake behavior is a worthy goal and demanded by society. Along these lines, one of the primary objectives of the Regional Earthquake Likelihood Models (RELM) working group was to formalize earthquake occurrence hypotheses in the form of prospective earthquake rate forecasts in California. RELM members, working in small research groups, developed more than a dozen 5-year forecasts; they also outlined a performance evaluation method and provided a conceptual description of a Testing Center in which to perform predictability experiments. Subsequently, researchers working within the Collaboratory for the Study of Earthquake Predictability (CSEP) have begun implementing Testing Centers in different locations worldwide, and the RELM predictability experiment-a truly prospective earthquake prediction effort-is underway within the U. S. branch of CSEP. The experiment, designed to compare time-invariant 5-year earthquake rate forecasts, is now approximately halfway to its completion. In this paper, we describe the models under evaluation and present, for the first time, preliminary results of this unique experiment. While these results are preliminary-the forecasts were meant for an application of 5 years-we find interesting results: most of the models are consistent with the observation and one model forecasts the distribution of earthquakes best. We discuss the observed sample of target earthquakes in the context of historical seismicity within the testing region, highlight potential pitfalls of the current tests, and suggest plans for future revisions to experiments such as this one. ?? 2010 The Author(s).

First Results of the Regional Earthquake Likelihood Models Experiment

NASA Astrophysics Data System (ADS)

Schorlemmer, Danijel; Zechar, J. Douglas; Werner, Maximilian J.; Field, Edward H.; Jackson, David D.; Jordan, Thomas H.

2010-08-01

The ability to successfully predict the future behavior of a system is a strong indication that the system is well understood. Certainly many details of the earthquake system remain obscure, but several hypotheses related to earthquake occurrence and seismic hazard have been proffered, and predicting earthquake behavior is a worthy goal and demanded by society. Along these lines, one of the primary objectives of the Regional Earthquake Likelihood Models (RELM) working group was to formalize earthquake occurrence hypotheses in the form of prospective earthquake rate forecasts in California. RELM members, working in small research groups, developed more than a dozen 5-year forecasts; they also outlined a performance evaluation method and provided a conceptual description of a Testing Center in which to perform predictability experiments. Subsequently, researchers working within the Collaboratory for the Study of Earthquake Predictability (CSEP) have begun implementing Testing Centers in different locations worldwide, and the RELM predictability experiment—a truly prospective earthquake prediction effort—is underway within the U.S. branch of CSEP. The experiment, designed to compare time-invariant 5-year earthquake rate forecasts, is now approximately halfway to its completion. In this paper, we describe the models under evaluation and present, for the first time, preliminary results of this unique experiment. While these results are preliminary—the forecasts were meant for an application of 5 years—we find interesting results: most of the models are consistent with the observation and one model forecasts the distribution of earthquakes best. We discuss the observed sample of target earthquakes in the context of historical seismicity within the testing region, highlight potential pitfalls of the current tests, and suggest plans for future revisions to experiments such as this one.

Climate services in the tourism sector - examples and market research

NASA Astrophysics Data System (ADS)

Damm, Andrea; Köberl, Judith; Prettenthaler, Franz; Kortschak, Dominik; Hofer, Marianne; Winkler, Claudia

2017-04-01

Tourism is one of the most weather-sensitive sectors. Hence, dealing with weather and climate risks is an important part of operational risk management. WEDDA® (WEather Driven Demand Analysis), developed by Joanneum Research, represents a comprehensive and flexible toolbox for managing weather and climate risks. Modelling the demand for products or services of a particular economic sector or company and its weather and climate sensitivity usually forms the starting and central point of WEDDA®. Coupling the calibrated demand models to either long-term climate scenarios or short-term weather forecasts enables the use of WEDDA® for the following areas of application: (i) implementing short-term forecasting systems for the prediction of the considered indicator; (ii) quantifying the weather risk of a particular economic sector or company using parameters from finance (e.g. Value-at-Risk); (iii) assessing the potential impacts of changing climatic conditions on a particular economic sector or company. WEDDA® for short-term forecasts on the demand for products or services is currently used by various tourism businesses, such as open-air swimming pools, ski areas, and restaurants. It supports tourism and recreation facilities to better cope with (increasing) weather variability by optimizing the disposability of staff, resources and merchandise according to expected demand. Since coping with increasing weather variability forms one of the challenges with respect to climate change, WEDDA® may become an important component within a whole pool of weather and climate services designed to support tourism and recreation facilities to adapt to climate change. Climate change impact assessments at European scale, as conducted in the EU-FP7 project IMPACT2C, provide basic information of climate change impacts on tourism demand not only for individual tourism businesses, but also for regional and national tourism planners and policy makers interested in benchmarks for the vulnerability of their tourism destination. In this project we analysed the impacts of +2 °C global warming on winter tourism demand in ski tourism related regions in Europe. In order to achieve the climate targets, tailored climate information services - for individual businesses as well as at the regional and national level - play an important role. The current market, however, is still in the early stages. In the ongoing H2020 projects EU-MACS (www.eu-macs.eu) and MARCO (www.marco-h2020.eu) (Nov 2016 - Oct 2018) Joanneum Research explores the climate services market in the tourism sector. The current use of climate services is reviewed in detail and in an interactive process key market barriers and enablers will be identified in close collaboration with stakeholders from the tourism industry. The analysis and co-development of new climate services concepts for the tourism sector aims to reduce the gaps between climate services supply and demand.

A Machine LearningFramework to Forecast Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in computational expense. The low computational cost (and by association low computer-power requirement) means that the machine learning algorithms could be installed on a wave-energy converter as a form of "edge computing" where a device could forecast its own 48-hour energy production.

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

An operational wave forecasting system for the east coast of India

NASA Astrophysics Data System (ADS)

Sandhya, K. G.; Murty, P. L. N.; Deshmukh, Aditya N.; Balakrishnan Nair, T. M.; Shenoi, S. S. C.

2018-03-01

Demand for operational ocean state forecasting is increasing, owing to the ever-increasing marine activities in the context of blue economy. In the present study, an operational wave forecasting system for the east coast of India is proposed using unstructured Simulating WAves Nearshore model (UNSWAN). This modelling system uses very high resolution mesh near the Indian east coast and coarse resolution offshore, and thus avoids the necessity of nesting with a global wave model. The model is forced with European Centre for Medium-Range Weather Forecasts (ECMWF) winds and simulates wave parameters and wave spectra for the next 3 days. The spatial pictures of satellite data overlaid on simulated wave height show that the model is capable of simulating the significant wave heights and their gradients realistically. Spectral validation has been done using the available data to prove the reliability of the model. To further evaluate the model performance, the wave forecast for the entire year 2014 is evaluated against buoy measurements over the region at 4 waverider buoy locations. Seasonal analysis of significant wave height (Hs) at the four locations showed that the correlation between the modelled and observed was the highest (in the range 0.78-0.96) during the post-monsoon season. The variability of Hs was also the highest during this season at all locations. The error statistics showed clear seasonal and geographical location dependence. The root mean square error at Visakhapatnam was the same (0.25) for all seasons, but it was the smallest for pre-monsoon season (0.12 m and 0.17 m) for Puducherry and Gopalpur. The wind sea component showed higher variability compared to the corresponding swell component in all locations and for all seasons. The variability was picked by the model to a reasonable level in most of the cases. The results of statistical analysis show that the modelling system is suitable for use in the operational scenario.

Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan.

PubMed

Juang, Wang-Chuan; Huang, Sin-Jhih; Huang, Fong-Dee; Cheng, Pei-Wen; Wann, Shue-Ren

2017-12-01

Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses. A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visit t =7111.161+(a t +0.37462 a t -1). The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Short-term Inundation Forecasting for Tsunamis Version 4.0 Brings Forecasting Speed, Accuracy, and Capability Improvements to NOAA's Tsunami Warning Centers

NASA Astrophysics Data System (ADS)

Sterling, K.; Denbo, D. W.; Eble, M. C.

2016-12-01

Short-term Inundation Forecasting for Tsunamis (SIFT) software was developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) for use in tsunami forecasting and has been used by both U.S. Tsunami Warning Centers (TWCs) since 2012, when SIFTv3.1 was operationally accepted. Since then, advancements in research and modeling have resulted in several new features being incorporated into SIFT forecasting. Following the priorities and needs of the TWCs, upgrades to SIFT forecasting were implemented into SIFTv4.0, scheduled to become operational in October 2016. Because every minute counts in the early warning process, two major time saving features were implemented in SIFT 4.0. To increase processing speeds and generate high-resolution flooding forecasts more quickly, the tsunami propagation and inundation codes were modified to run on Graphics Processing Units (GPUs). To reduce time demand on duty scientists during an event, an automated DART inversion (or fitting) process was implemented. To increase forecasting accuracy, the forecasted amplitudes and inundations were adjusted to include dynamic tidal oscillations, thereby reducing the over-estimates of flooding common in SIFTv3.1 due to the static tide stage conservatively set at Mean High Water. Further improvements to forecasts were gained through the assimilation of additional real-time observations. Cabled array measurements from Bottom Pressure Recorders (BPRs) in the Oceans Canada NEPTUNE network are now available to SIFT for use in the inversion process. To better meet the needs of harbor masters and emergency managers, SIFTv4.0 adds a tsunami currents graphical product to the suite of disseminated forecast results. When delivered, these new features in SIFTv4.0 will improve the operational tsunami forecasting speed, accuracy, and capabilities at NOAA's Tsunami Warning Centers.

A decision support tool for sustainable planning of urban water systems: presenting the Dynamic Urban Water Simulation Model.

PubMed

Willuweit, Lars; O'Sullivan, John J

2013-12-15

Population growth, urbanisation and climate change represent significant pressures on urban water resources, requiring water managers to consider a wider array of management options that account for economic, social and environmental factors. The Dynamic Urban Water Simulation Model (DUWSiM) developed in this study links urban water balance concepts with the land use dynamics model MOLAND and the climate model LARS-WG, providing a platform for long term planning of urban water supply and water demand by analysing the effects of urbanisation scenarios and climatic changes on the urban water cycle. Based on potential urbanisation scenarios and their effects on a city's water cycle, DUWSiM provides the functionality for assessing the feasibility of centralised and decentralised water supply and water demand management options based on forecasted water demand, stormwater and wastewater generation, whole life cost and energy and potential for water recycling. DUWSiM has been tested using data from Dublin, the capital of Ireland, and it has been shown that the model is able to satisfactorily predict water demand and stormwater runoff. Copyright © 2013 Elsevier Ltd. All rights reserved.

The case for probabilistic forecasting in hydrology

NASA Astrophysics Data System (ADS)

Krzysztofowicz, Roman

2001-08-01

That forecasts should be stated in probabilistic, rather than deterministic, terms has been argued from common sense and decision-theoretic perspectives for almost a century. Yet most operational hydrological forecasting systems produce deterministic forecasts and most research in operational hydrology has been devoted to finding the 'best' estimates rather than quantifying the predictive uncertainty. This essay presents a compendium of reasons for probabilistic forecasting of hydrological variates. Probabilistic forecasts are scientifically more honest, enable risk-based warnings of floods, enable rational decision making, and offer additional economic benefits. The growing demand for information about risk and the rising capability to quantify predictive uncertainties create an unparalleled opportunity for the hydrological profession to dramatically enhance the forecasting paradigm.

Probabilistic calibration of the distributed hydrological model RIBS applied to real-time flood forecasting: the Harod river basin case study (Israel)

NASA Astrophysics Data System (ADS)

Nesti, Alice; Mediero, Luis; Garrote, Luis; Caporali, Enrica

2010-05-01

An automatic probabilistic calibration method for distributed rainfall-runoff models is presented. The high number of parameters in hydrologic distributed models makes special demands on the optimization procedure to estimate model parameters. With the proposed technique it is possible to reduce the complexity of calibration while maintaining adequate model predictions. The first step of the calibration procedure of the main model parameters is done manually with the aim to identify their variation range. Afterwards a Monte-Carlo technique is applied, which consists on repetitive model simulations with randomly generated parameters. The Monte Carlo Analysis Toolbox (MCAT) includes a number of analysis methods to evaluate the results of these Monte Carlo parameter sampling experiments. The study investigates the use of a global sensitivity analysis as a screening tool to reduce the parametric dimensionality of multi-objective hydrological model calibration problems, while maximizing the information extracted from hydrological response data. The method is applied to the calibration of the RIBS flood forecasting model in the Harod river basin, placed on Israel. The Harod basin has an extension of 180 km2. The catchment has a Mediterranean climate and it is mainly characterized by a desert landscape, with a soil that is able to absorb large quantities of rainfall and at the same time is capable to generate high peaks of discharge. Radar rainfall data with 6 minute temporal resolution are available as input to the model. The aim of the study is the validation of the model for real-time flood forecasting, in order to evaluate the benefits of improved precipitation forecasting within the FLASH European project.

Trading Off Global Fuel Supply, CO2 Emissions and Sustainable Development.

PubMed

Wagner, Liam; Ross, Ian; Foster, John; Hankamer, Ben

2016-01-01

The United Nations Conference on Climate Change (Paris 2015) reached an international agreement to keep the rise in global average temperature 'well below 2°C' and to 'aim to limit the increase to 1.5°C'. These reductions will have to be made in the face of rising global energy demand. Here a thoroughly validated dynamic econometric model (Eq 1) is used to forecast global energy demand growth (International Energy Agency and BP), which is driven by an increase of the global population (UN), energy use per person and real GDP (World Bank and Maddison). Even relatively conservative assumptions put a severe upward pressure on forecast global energy demand and highlight three areas of concern. First, is the potential for an exponential increase of fossil fuel consumption, if renewable energy systems are not rapidly scaled up. Second, implementation of internationally mandated CO2 emission controls are forecast to place serious constraints on fossil fuel use from ~2030 onward, raising energy security implications. Third is the challenge of maintaining the international 'pro-growth' strategy being used to meet poverty alleviation targets, while reducing CO2 emissions. Our findings place global economists and environmentalists on the same side as they indicate that the scale up of CO2 neutral renewable energy systems is not only important to protect against climate change, but to enhance global energy security by reducing our dependence of fossil fuels and to provide a sustainable basis for economic development and poverty alleviation. Very hard choices will have to be made to achieve 'sustainable development' goals.

Trading Off Global Fuel Supply, CO2 Emissions and Sustainable Development

PubMed Central

Wagner, Liam; Ross, Ian; Foster, John; Hankamer, Ben

2016-01-01

The United Nations Conference on Climate Change (Paris 2015) reached an international agreement to keep the rise in global average temperature ‘well below 2°C’ and to ‘aim to limit the increase to 1.5°C’. These reductions will have to be made in the face of rising global energy demand. Here a thoroughly validated dynamic econometric model (Eq 1) is used to forecast global energy demand growth (International Energy Agency and BP), which is driven by an increase of the global population (UN), energy use per person and real GDP (World Bank and Maddison). Even relatively conservative assumptions put a severe upward pressure on forecast global energy demand and highlight three areas of concern. First, is the potential for an exponential increase of fossil fuel consumption, if renewable energy systems are not rapidly scaled up. Second, implementation of internationally mandated CO2 emission controls are forecast to place serious constraints on fossil fuel use from ~2030 onward, raising energy security implications. Third is the challenge of maintaining the international ‘pro-growth’ strategy being used to meet poverty alleviation targets, while reducing CO2 emissions. Our findings place global economists and environmentalists on the same side as they indicate that the scale up of CO2 neutral renewable energy systems is not only important to protect against climate change, but to enhance global energy security by reducing our dependence of fossil fuels and to provide a sustainable basis for economic development and poverty alleviation. Very hard choices will have to be made to achieve ‘sustainable development’ goals. PMID:26959977

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.

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.).

"Near-term" Natural Catastrophe Risk Management and Risk Hedging in a Changing Climate

NASA Astrophysics Data System (ADS)

Michel, Gero; Tiampo, Kristy

2014-05-01

Competing with analytics - Can the insurance market take advantage of seasonal or "near-term" forecasting and temporal changes in risk? Natural perils (re)insurance has been based on models following climatology i.e. the long-term "historical" average. This is opposed to considering the "near-term" and forecasting hazard and risk for the seasons or years to come. Variability and short-term changes in risk are deemed abundant for almost all perils. In addition to hydrometeorological perils whose changes are vastly discussed, earthquake activity might also change over various time-scales affected by earlier local (or even global) events, regional changes in the distribution of stresses and strains and more. Only recently has insurance risk modeling of (stochastic) hurricane-years or extratropical-storm-years started considering our ability to forecast climate variability herewith taking advantage of apparent correlations between climate indicators and the activity of storm events. Once some of these "near-term measures" were in the market, rating agencies and regulators swiftly adopted these concepts demanding companies to deploy a selection of more conservative "time-dependent" models. This was despite the fact that the ultimate effect of some of these measures on insurance risk was not well understood. Apparent short-term success over the last years in near-term seasonal hurricane forecasting was brought to a halt in 2013 when these models failed to forecast the exceptional shortage of hurricanes herewith contradicting an active-year forecast. The focus of earthquake forecasting has in addition been mostly on high rather than low temporal and regional activity despite the fact that avoiding losses does not by itself create a product. This presentation sheds light on new risk management concepts for over-regional and global (re)insurance portfolios that take advantage of forecasting changes in risk. The presentation focuses on the "upside" and on new opportunities in risk-taking rather than the "downside" and the general notion that catastrophes will get worse. The focus will be on the industry's ability to hedge and optimize risk more efficiently in a changing environment.

Two-stage seasonal streamflow forecasts to guide water resources decisions and water rights allocation

NASA Astrophysics Data System (ADS)

Block, P. J.; Gonzalez, E.; Bonnafous, L.

2011-12-01

Decision-making in water resources is inherently uncertain producing copious risks, ranging from operational (present) to planning (season-ahead) to design/adaptation (decadal) time-scales. These risks include human activity and climate variability/change. As the risks in designing and operating water systems and allocating available supplies vary systematically in time, prospects for predicting and managing such risks become increasingly attractive. Considerable effort has been undertaken to improve seasonal forecast skill and advocate for integration to reduce risk, however only minimal adoption is evident. Impediments are well defined, yet tailoring forecast products and allowing for flexible adoption assist in overcoming some obstacles. The semi-arid Elqui River basin in Chile is contending with increasing levels of water stress and demand coupled with insufficient investment in infrastructure, taxing its ability to meet agriculture, hydropower, and environmental requirements. The basin is fed from a retreating glacier, with allocation principles founded on a system of water rights and markets. A two-stage seasonal streamflow forecast at leads of one and two seasons prescribes the probability of reductions in the value of each water right, allowing water managers to inform their constituents in advance. A tool linking the streamflow forecast to a simple reservoir decision model also allows water managers to select a level of confidence in the forecast information.

Estimating household water demand using revealed and contingent behaviors: Evidence from Vietnam

NASA Astrophysics Data System (ADS)

Cheesman, Jeremy; Bennett, Jeff; Son, Tran Vo Hung

2008-11-01

This article estimates the water demand of households using (1) municipal water exclusively and (2) municipal water and household well water in the capital city of Dak Lak Province in Vietnam. Household water demands are estimated using a panel data set formed by pooling household records of metered municipal water consumption and their stated preferences for water consumption contingent on hypothetical water prices. Estimates show that households using municipal water exclusively have very price inelastic demand. Households using municipal and household well water have more price elastic, but still inelastic, simultaneous water demand and treat municipal water and household well water as substitutes. Household water consumption is influenced by household water storage and supply infrastructure, income, and socioeconomic attributes. The demand estimates are used to forecast municipal water consumption by households in Buon Ma Thuot following an increase to the municipal water tariff to forecast the municipal water supply company's revenue stream following a tariff increase and to estimate the consumer surplus loss resulting from municipal water supply shortages.

Flood Warning and Forecasting System in Slovakia

NASA Astrophysics Data System (ADS)

Leskova, Danica

2016-04-01

In 2015, it finished project Flood Warning and Forecasting System (POVAPSYS) as part of the flood protection in Slovakia till 2010. The aim was to build POVAPSYS integrated computerized flood forecasting and warning system. It took a qualitatively higher level of output meteorological and hydrological services in case of floods affecting large territorial units, as well as local flood events. It is further unfolding demands on performance and coordination of meteorological and hydrological services, troubleshooting observation, evaluation of data, fast communication, modeling and forecasting of meteorological and hydrological processes. Integration of all information entering and exiting to and from the project POVAPSYS provides Hydrological Flood Forecasting System (HYPOS). The system provides information on the current hydrometeorological situation and its evolution with the generation of alerts and notifications in case of exceeding predefined thresholds. HYPOS's functioning of the system requires flawless operability in critical situations while minimizing the loss of its key parts. HYPOS is a core part of the project POVAPSYS, it is a comprehensive software solutions based on a modular principle, providing data and processed information including alarms, in real time. In order to achieve full functionality of the system, in proposal, we have put emphasis on reliability, robustness, availability and security.

Understanding urban travel demand : problems, solutions, and the role of forecasting

DOT National Transportation Integrated Search

1999-08-01

This report is a general examination and critique of transportation policy making, focusing on the role of traffic and land use forecasting. There are four major components: (1) Current, historical, and projected travel behavior in the Twin Cities; (...

Technology requirements for post-1985 communications satellites

NASA Technical Reports Server (NTRS)

Burtt, J. E.; Moe, C. R.; Elms, R. V.; Delateur, L. A.; Sedlacek, W. C.; Younger, G. G.

1973-01-01

The technical and functional requirements for commercial communication satellites are discussed. The need for providing quality service at an acceptable cost is emphasized. Specialized services are postulated in a needs model which forecasts future demands. This needs model is based upon 322 separately identified needs for long distance communication. It is shown that the 1985 demand for satellite communication service for a domestic region such as the United States, and surrounding sea and air lanes, may require on the order of 100,000 MHz of bandwith. This level of demand can be met by means of the presently allocated bandwidths and developing some key technologies. Suggested improvements include: (1) improving antennas so that high speed switching will be possible; (2) development of solid state transponders for 12 GHz and possibly higher frequencies; (3) development of switched or steered beam antennas with 10 db or higher gain for aircraft; and (4) continued development of improved video channel compression techniques and hardware.

Analysis of Market Opportunities for Chinese Private Express Delivery Industry

NASA Astrophysics Data System (ADS)

Jiang, Changbing; Bai, Lijun; Tong, Xiaoqing

China's express delivery market has become the arena in which each express enterprise struggles to chase due to the huge potential demand and high profitable prospects. So certain qualitative and quantitative forecast for the future changes of China's express delivery market will help enterprises understand various types of market conditions and social changes in demand and adjust business activities to enhance their competitiveness timely. The development of China's express delivery industry is first introduced in this chapter. Then the theoretical basis of the regression model is overviewed. We also predict the demand trends of China's express delivery market by using Pearson correlation analysis and regression analysis from qualitative and quantitative aspects, respectively. Finally, we draw some conclusions and recommendations for China's express delivery industry.

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.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

PubMed Central

Jensen, Tue V.; Pinson, Pierre

2017-01-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation. PMID:29182600

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system.

PubMed

Jensen, Tue V; Pinson, Pierre

2017-11-28

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

Solar EUV irradiance for space weather applications

NASA Astrophysics Data System (ADS)

Viereck, R. A.

2015-12-01

Solar EUV irradiance is an important driver of space weather models. Large changes in EUV and x-ray irradiances create large variability in the ionosphere and thermosphere. Proxies such as the F10.7 cm radio flux, have provided reasonable estimates of the EUV flux but as the space weather models become more accurate and the demands of the customers become more stringent, proxies are no longer adequate. Furthermore, proxies are often provided only on a daily basis and shorter time scales are becoming important. Also, there is a growing need for multi-day forecasts of solar EUV irradiance to drive space weather forecast models. In this presentation we will describe the needs and requirements for solar EUV irradiance information from the space weather modeler's perspective. We will then translate these requirements into solar observational requirements such as spectral resolution and irradiance accuracy. We will also describe the activities at NOAA to provide long-term solar EUV irradiance observations and derived products that are needed for real-time space weather modeling.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

NASA Astrophysics Data System (ADS)

Jensen, Tue V.; Pinson, Pierre

2017-11-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

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.

178: FORECASTING THE SHORTAGE OF NEUROSURGEONS IN IRAN USING A SYSTEM DYNAMICS MODEL APPROACH

PubMed Central

Ezzatabadi, Mohammad Ranjbar; Zadeh, Sina Abdollah; Rafiei, Sima

2017-01-01

Background and aims Shortage of physicians particularly in specialty levels is considered as an important issue in Iran health system. Thus in an uncertain environment, long term planning is required for health professionals as a basic priority on a national scale. The study aimed to estimate the number of required neurosurgeons using system dynamic modelling. Methods System dynamic modelling was applied to predict the gap between stock and number of required neurosurgeons in Iran up to 2020. A supply and demand simulation model was constructed for neurosurgeons using system dynamic approach. The demand model included epidemiological, demographic and utilization variables. Along with, supply model incorporated current stock of neurosurgeons and flow variables such as: attrition, migration and retirement rate. Data were obtained from various governmental databases were analysed by Vensim PLE Version 3.0 to address the flow of health professionals, clinical infrastructure, population demographics and disease prevalence during the time. Results It was forecasted that shortage in number of neurosurgeons would disappear at 2020. The most dominant determinants on predicted number of neurosurgeons were the prevalence of neurosurgical diseases, the rate for service utilization and medical capacity of the region. Conclusion Results of the study suggests that shortage of neurosurgeons in some areas of the country relates to maldistribution of the specialists. Accordingly there is a need to reconsider the allocation system for health professionals within the country instead of increasing the overall number of acceptance quota in training positions.

Implementation of a limited area ensemble meteorological system in a GRID and HPC environment

NASA Astrophysics Data System (ADS)

Marrocu, M.; Pusceddu, G.; Peneva, E.

2009-04-01

At present there is an ever increasing demand for reliable probabilistic short range weather forecast. At regional scale this kind of forecast is even more valuable especially in cases of high impact weather such as violent extra tropical storms which may result in loss of lives and properties due to wide-spread flooding and gale force winds. The Multi-Analysis Multi-Model (MAMM) approach is a relatively new method of Ensemble Prediction System (EPS) in which the single deterministic forecasts are combined using specific statistical techniques to estimate the probability of a certain event to develop. MAMM simulations are very demanding in terms of computer resources, requiring CPU time and storage capacity which a single organization is hardly to provide. Moreover operational short range weather prediction at regional scale is a time critical task wich has to be completed in less than a couple of hours to be really usable. In this presentation we will discuss the results of two project (GRIDA3: grida3.crs4.it, CYBERSAR: www.cybersar.com) financed by the Italian Ministry of Research (MIUR) in which we have implemented a MAMM EPS based on 3 limited area models (BOLAM, MM5 and WRF) fed with two different sets of Initial and Boundary Conditions (NCEP and ECMWF). The system has been ported on a PC cluster and made accessible by a specifically designed web portal (grida3.crs4.it/enginframe/premiagrid). This allows an authorized user to use the remote HPC resources behind the web portal, and to run the service (named PREMIAGRID) on demand setting only three parameter: the place in the world, the initial date and the time of integration. Results obtained with the porting of the PREMIAGRID service in the virtual grid of the CyberSAR project using the gLite middleware, will be also discussed.

Forecasting the regional distribution and sufficiency of physicians in Japan with a coupled system dynamics-geographic information system model.

PubMed

Ishikawa, Tomoki; Fujiwara, Kensuke; Ohba, Hisateru; Suzuki, Teppei; Ogasawara, Katsuhiko

2017-09-12

In Japan, the shortage of physicians has been recognized as a major medical issue. In our previous study, we reported that the absolute shortage will be resolved in the long term, but maldistribution among specialties will persist. To address regional shortage, several Japanese medical schools increased existing quota and established "regional quotas." This study aims to assist policy makers in designing effective policies; we built a model for forecasting physician numbers by region to evaluate future physician supply-demand balances. For our case study, we selected Hokkaido Prefecture in Japan, a region displaying disparities in healthcare services availability between urban and rural areas. We combined a system dynamics (SD) model with geographic information system (GIS) technology to analyze the dynamic change in spatial distribution of indicators. For Hokkaido overall and for each secondary medical service area (SMSA) within the prefecture, we analyzed the total number of practicing physicians. For evaluating absolute shortage and maldistribution, we calculated sufficiency levels and Gini coefficient. Our study covered the period 2010-2030 in 5-year increments. According to our forecast, physician shortage in Hokkaido Prefecture will largely be resolved by 2020. Based on current policies, we forecast that four SMSAs in Hokkaido will continue to experience physician shortages past that date, but only one SMSA would still be understaffed in 2030. The results show the possibility that diminishing imbalances between SMSAs would not necessarily mean that regional maldistribution would be eliminated, as seen from the sufficiency levels of the various SMSAs. Urgent steps should be taken to place doctors in areas where our forecasting model predicts that physician shortages could occur in the future.

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

Predicting optimum crop designs using crop models and seasonal climate forecasts.

PubMed

Rodriguez, D; de Voil, P; Hudson, D; Brown, J N; Hayman, P; Marrou, H; Meinke, H

2018-02-02

Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed. Here we test our capacity to inform that "hindsight", by linking a tested crop model (APSIM) with a skillful seasonal climate forecasting system, to answer "What is the value of the skill in seasonal climate forecasting, to inform crop designs?" Results showed that the GCM POAMA-2 was reliable and skillful, and that when linked with APSIM, optimum crop designs could be informed. We conclude that reliable and skillful GCMs that are easily interfaced with crop simulation models, can be used to inform optimum crop designs, increase farmers profits and reduce risks.

The 30/20 GHz fixed communications systems service demand assessment. Volume 2: Main report

NASA Technical Reports Server (NTRS)

Gamble, R. B.; Seltzer, H. R.; Speter, K. M.; Westheimer, M.

1979-01-01

A forecast of demand for telecommunications services through the year 2000 is presented with particular reference to demand for satellite communications. Estimates of demand are provided for voice, video, and data services and for various subcategories of these services. The results are converted to a common digital measure in terms of terabits per year and aggregated to obtain total demand projections.

Super Ensemble-based Aviation Turbulence Guidance (SEATG) for Air Traffic Management (ATM)

NASA Astrophysics Data System (ADS)

Kim, Jung-Hoon; Chan, William; Sridhar, Banavar; Sharman, Robert

2014-05-01

Super Ensemble (ensemble of ten turbulence metrics from time-lagged ensemble members of weather forecast data)-based Aviation Turbulence Guidance (SEATG) is developed using Weather Research and Forecasting (WRF) model and in-situ eddy dissipation rate (EDR) observations equipped on commercial aircraft over the contiguous United States. SEATG is a sequence of five procedures including weather modeling, calculating turbulence metrics, mapping EDR-scale, evaluating metrics, and producing final SEATG forecast. This uses similar methodology to the operational Graphic Turbulence Guidance (GTG) with three major improvements. First, SEATG use a higher resolution (3-km) WRF model to capture cloud-resolving scale phenomena. Second, SEATG computes turbulence metrics for multiple forecasts that are combined at the same valid time resulting in an time-lagged ensemble of multiple turbulence metrics. Third, SEATG provides both deterministic and probabilistic turbulence forecasts to take into account weather uncertainties and user demands. It is found that the SEATG forecasts match well with observed radar reflectivity along a surface front as well as convectively induced turbulence outside the clouds on 7-8 Sep 2012. And, overall performance skill of deterministic SEATG against the observed EDR data during this period is superior to any single turbulence metrics. Finally, probabilistic SEATG is used as an example application of turbulence forecast for air-traffic management. In this study, a simple Wind-Optimal Route (WOR) passing through the potential areas of probabilistic SEATG and Lateral Turbulence Avoidance Route (LTAR) taking into account the SEATG are calculated at z = 35000 ft (z = 12 km) from Los Angeles to John F. Kennedy international airports. As a result, WOR takes total of 239 minutes with 16 minutes of SEATG areas for 40% of moderate turbulence potential, while LTAR takes total of 252 minutes travel time that 5% of fuel would be additionally consumed to entirely avoid the moderate SEATG regions.

Modeling Weather Impact on Ground Delay Programs

NASA Technical Reports Server (NTRS)

Wang, Yao; Kulkarni, Deepak

2011-01-01

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

A multi-period optimization model for energy planning with CO(2) emission consideration.

PubMed

Mirzaesmaeeli, H; Elkamel, A; Douglas, P L; Croiset, E; Gupta, M

2010-05-01

A novel deterministic multi-period mixed-integer linear programming (MILP) model for the power generation planning of electric systems is described and evaluated in this paper. The model is developed with the objective of determining the optimal mix of energy supply sources and pollutant mitigation options that meet a specified electricity demand and CO(2) emission targets at minimum cost. Several time-dependent parameters are included in the model formulation; they include forecasted energy demand, fuel price variability, construction lead time, conservation initiatives, and increase in fixed operational and maintenance costs over time. The developed model is applied to two case studies. The objective of the case studies is to examine the economical, structural, and environmental effects that would result if the electricity sector was required to reduce its CO(2) emissions to a specified limit. Copyright 2009 Elsevier Ltd. All rights reserved.

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

Forecasting Brassica rapa: Merging climate models with genotype specific process models for evaluation whole species response to climate change.

NASA Astrophysics Data System (ADS)

Pleban, J. R.; Mackay, D. S.; Ewers, B. E.; Weinig, C.; Guadagno, C. L.

2016-12-01

Human society has modified agriculture management practices and utilized a variety of breeding approaches to adapt to changing environments. Presently a dual pronged challenge has emerged as environmental change is occurring more rapidly while the demand of population growth on food supply is rising. Knowledge of how current agricultural practices will respond to these challenges can be informed through crafted prognostic modeling approaches. Amongst the uncertainties associated with forecasting agricultural production in a changing environment is evaluation of the responses across the existing genotypic diversity of crop species. Mechanistic models of plant productivity provide a means of genotype level parameterization allowing for a prognostic evaluation of varietal performance under changing climate. Brassica rapa represents an excellent species for this type of investigation because of its wide cultivation as well as large morphological and physiological diversity. We incorporated genotypic parameterization of B. rapa genotypes based on unique CO2 assimilation strategies, vulnerabilities to cavitation, and root to leaf area relationships into the TREES model. Three climate drivers, following the "business-as-usual" greenhouse gas emissions scenario (RCP 8.5) from Coupled Model Intercomparison Project, Phase 5 (CMIP5) were considered: temperature (T) along with associated changes in vapor pressure deficit (VPD), increasing CO2, as well as alternatives in irrigation regime across a temporal scale of present day to 2100. Genotypic responses to these drivers were evaluated using net primary productivity (NPP) and percent loss hydraulic conductance (PLC) as a measure of tolerance for a particular watering regime. Genotypic responses to T were witnessed as water demand driven by increases in VPD at 2050 and 2100 drove some genotypes to greater PLC and in a subset of these saw periodic decreases in NPP during a growing season. Genotypes able to withstand the greater water demand showed lower NPP yields relative to hydraulically aggressive genotypes but saw limited PLC. Expansion of this analysis to large recombinant inbred populations may inform breeders in identification of trait combinations needed to meet the coupled challenge of rapid environmental change and increase food demand.

Semi-nonparametric VaR forecasts for hedge funds during the recent crisis

NASA Astrophysics Data System (ADS)

Del Brio, Esther B.; Mora-Valencia, Andrés; Perote, Javier

2014-05-01

The need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important literature in econophysics. Although these accurate VaR models and methodologies are particularly demanded for hedge fund managers, there exist few articles specifically devoted to implement new techniques in hedge fund returns VaR forecasting. This article advances in these issues by comparing the performance of risk measures based on parametric distributions (the normal, Student’s t and skewed-t), semi-nonparametric (SNP) methodologies based on Gram-Charlier (GC) series and the extreme value theory (EVT) approach. Our results show that normal-, Student’s t- and Skewed t- based methodologies fail to forecast hedge fund VaR, whilst SNP and EVT approaches accurately success on it. We extend these results to the multivariate framework by providing an explicit formula for the GC copula and its density that encompasses the Gaussian copula and accounts for non-linear dependences. We show that the VaR obtained by the meta GC accurately captures portfolio risk and outperforms regulatory VaR estimates obtained through the meta Gaussian and Student’s t distributions.

Learning-based Wind Estimation using Distant Soundings for Unguided Aerial Delivery

NASA Astrophysics Data System (ADS)

Plyler, M.; Cahoy, K.; Angermueller, K.; Chen, D.; Markuzon, N.

2016-12-01

Delivering unguided, parachuted payloads from aircraft requires accurate knowledge of the wind field inside an operational zone. Usually, a dropsonde released from the aircraft over the drop zone gives a more accurate wind estimate than a forecast. Mission objectives occasionally demand releasing the dropsonde away from the drop zone, but still require accuracy and precision. Barnes interpolation and many other assimilation methods do poorly when the forecast error is inconsistent in a forecast grid. A machine learning approach can better leverage non-linear relations between different weather patterns and thus provide a better wind estimate at the target drop zone when using data collected up to 100 km away. This study uses the 13 km resolution Rapid Refresh (RAP) dataset available through NOAA and subsamples to an area around Yuma, AZ and up to approximately 10km AMSL. RAP forecast grids are updated with simulated dropsondes taken from analysis (historical weather maps). We train models using different data mining and machine learning techniques, most notably boosted regression trees, that can accurately assimilate the distant dropsonde. The model takes a forecast grid and simulated remote dropsonde data as input and produces an estimate of the wind stick over the drop zone. Using ballistic winds as a defining metric, we show our data driven approach does better than Barnes interpolation under some conditions, most notably when the forecast error is different between the two locations, on test data previously unseen by the model. We study and evaluate the model's performance depending on the size, the time lag, the drop altitude, and the geographic location of the training set, and identify parameters most contributing to the accuracy of the wind estimation. This study demonstrates a new approach for assimilating remotely released dropsondes, based on boosted regression trees, and shows improvement in wind estimation over currently used methods.

Forecasting Demand for Civilian Pilots: A Cost Savings Approach to Managing Air Force Pilot Resources

DTIC Science & Technology

2007-03-01

terror attacks led people to believe that flying was unsafe. The Breusch - Pagan /Cook-Weisberg test for heteroskedasticity was performed to find...would yield an increase of 13.8 million revenue passenger miles. As with the previous model, the Breusch - Pagan /Cook-Weisberg test for...Appendix A. Data Set of Variables Studied ............................................................ 31 Appendix B. Stationarity Tests

The Impact of a Vehicle-to-Vehicle Communications Rulemaking on Growth in the DSRC Automotive Aftermarket A Market Adoption Model and Forecast for Dedicated Short Range Communications (DSRC) for Light and Heavy Vehicle Categories

DOT National Transportation Integrated Search

2016-10-15

The focus of this project was to estimate the potential impact of a new motor vehicle government mandate for vehicle-to-vehicle (V2V) technology on the demand for aftermarket devices, applications, and infrastructure that leverages the same dedicated...

An Interim Update to the 2035 Socioeconomic and Travel Demand Forecasts for Virginia

DOT National Transportation Integrated Search

2012-10-01

In support of the update to Virginias 2035 Statewide Multimodal Plan, this report provides an update to select : socioeconomic forecasts initially made in 2009 based on a review of data from national sources and the literature. Mobility : needs ex...

An interim update to the 2035 socioeconomic and travel demand forecasts for Virginia.

DOT National Transportation Integrated Search

2012-09-01

"In support of the update to Virginias 2035 Statewide Multimodal Plan, this report provides an update to select : socioeconomic forecasts initially made in 2009 based on a review of data from national sources and the literature. Mobility : needs e...

An interim update to the 2035 socioeconomic and travel demand forecasts for Virginia.

DOT National Transportation Integrated Search

2012-10-01

In support of the update to Virginias 2035 Statewide Multimodal Plan, this report provides an update to select : socioeconomic forecasts initially made in 2009 based on a review of data from national sources and the literature. Mobility : needs ex...

Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System

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

NONE

1998-01-01

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. The NEMS Commercial Sector Demand Module is a simulation tool based upon economic and engineering relationships that models commercial sector energy demands at the nine Census Division level of detail for eleven distinct categories of commercial buildings. Commercial equipment selections are performed for the major fuels of electricity, natural gas,more » and distillate fuel, for the major services of space heating, space cooling, water heating, ventilation, cooking, refrigeration, and lighting. The algorithm also models demand for the minor fuels of residual oil, liquefied petroleum gas, steam coal, motor gasoline, and kerosene, the renewable fuel sources of wood and municipal solid waste, and the minor services of office equipment. Section 2 of this report discusses the purpose of the model, detailing its objectives, primary input and output quantities, and the relationship of the Commercial Module to the other modules of the NEMS system. Section 3 of the report describes the rationale behind the model design, providing insights into further assumptions utilized in the model development process to this point. Section 3 also reviews alternative commercial sector modeling methodologies drawn from existing literature, providing a comparison to the chosen approach. Section 4 details the model structure, using graphics and text to illustrate model flows and key computations.« less

Forecasting of natural gas consumption with neural network and neuro fuzzy system

NASA Astrophysics Data System (ADS)

Kaynar, Oguz; Yilmaz, Isik; Demirkoparan, Ferhan

2010-05-01

The prediction of natural gas consumption is crucial for Turkey which follows foreign-dependent policy in point of providing natural gas and whose stock capacity is only 5% of internal total consumption. Prediction accuracy of demand is one of the elements which has an influence on sectored investments and agreements about obtaining natural gas, so on development of sector. In recent years, new techniques, such as artificial neural networks and fuzzy inference systems, have been widely used in natural gas consumption prediction in addition to classical time series analysis. In this study, weekly natural gas consumption of Turkey has been predicted by means of three different approaches. The first one is Autoregressive Integrated Moving Average (ARIMA), which is classical time series analysis method. The second approach is the Artificial Neural Network. Two different ANN models, which are Multi Layer Perceptron (MLP) and Radial Basis Function Network (RBFN), are employed to predict natural gas consumption. The last is Adaptive Neuro Fuzzy Inference System (ANFIS), which combines ANN and Fuzzy Inference System. Different prediction models have been constructed and one model, which has the best forecasting performance, is determined for each method. Then predictions are made by using these models and results are compared. Keywords: ANN, ANFIS, ARIMA, Natural Gas, Forecasting

Air Traffic Demand Estimates for 1995

DOT National Transportation Integrated Search

1975-04-01

The forecasts provide a range of reasonable 1995 activity levels for analyzing and comparing cost and performance characteristics of future air traffic management system concept alternatives. High and low estimates of the various demand measures are ...

scoringRules - A software package for probabilistic model evaluation

NASA Astrophysics Data System (ADS)

Lerch, Sebastian; Jordan, Alexander; Krüger, Fabian

2016-04-01

Models in the geosciences are generally surrounded by uncertainty, and being able to quantify this uncertainty is key to good decision making. Accordingly, probabilistic forecasts in the form of predictive distributions have become popular over the last decades. With the proliferation of probabilistic models arises the need for decision theoretically principled tools to evaluate the appropriateness of models and forecasts in a generalized way. Various scoring rules have been developed over the past decades to address this demand. Proper scoring rules are functions S(F,y) which evaluate the accuracy of a forecast distribution F , given that an outcome y was observed. As such, they allow to compare alternative models, a crucial ability given the variety of theories, data sources and statistical specifications that is available in many situations. This poster presents the software package scoringRules for the statistical programming language R, which contains functions to compute popular scoring rules such as the continuous ranked probability score for a variety of distributions F that come up in applied work. Two main classes are parametric distributions like normal, t, or gamma distributions, and distributions that are not known analytically, but are indirectly described through a sample of simulation draws. For example, Bayesian forecasts produced via Markov Chain Monte Carlo take this form. Thereby, the scoringRules package provides a framework for generalized model evaluation that both includes Bayesian as well as classical parametric models. The scoringRules package aims to be a convenient dictionary-like reference for computing scoring rules. We offer state of the art implementations of several known (but not routinely applied) formulas, and implement closed-form expressions that were previously unavailable. Whenever more than one implementation variant exists, we offer statistically principled default choices.

IWR-MAIN Water Use Forecasting System. Version 5.1. User’s Manual and System Description

DTIC Science & Technology

1987-12-01

Crosschecks for Input Data 1-68 11-1 Organization of the IWR-MAIN System H-8 11-2 Example of Econometric Demand Model 11-9 11-3 Example of Unit Use Coefficient...Unaccounted (entry does not affect default Loss and free service calculations) Y Conservation Data City Name: Test City USA Fl-Hetp, F2-return to monu, F4...socioeconomic data. 1-11 (1) Internal Growth Models The IWR-MAIN program contains a subroutine called GROWTH which uses econometric growth models based on

A flexible hydrological warning system in Denmark for real-time surface water and groundwater simulations

NASA Astrophysics Data System (ADS)

He, Xin; Stisen, Simon; Wiese, Marianne B.; Jørgen Henriksen, Hans

2015-04-01

In Denmark, increasing focus on extreme weather events has created considerable demand for short term forecasts and early warnings in relation to groundwater and surface water flooding. The Geological Survey of Denmark and Greenland (GEUS) has setup, calibrated and applied a nationwide water resources model, the DK-Model, primarily for simulating groundwater and surface water flows and groundwater levels during the past 20 years. So far, the DK-model has only been used in offline historical and future scenario simulations. Therefore, challenges arise in operating such a model for online forecasts and early warnings, which requires access to continuously updated observed climate input data and forecast data of precipitation, temperature and global radiation for the next 48 hours or longer. GEUS has a close collaboration with the Danish Meteorological Institute in order to test and enable this data input for the DK model. Due to the comprehensive physical descriptions of the DK-Model, the simulation results can potentially be any component of the hydrological cycle within the models domain. Therefore, it is important to identify which results need to be updated and saved in the real-time mode, since it is not computationally economical to save every result considering the heavy load of data. GEUS have worked closely with the end-users and interest groups such as water planners and emergency managers from the municipalities, water supply and waste water companies, consulting companies and farmer organizations, in order to understand their possible needs for real time simulation and monitoring of the nationwide water cycle. This participatory process has been supported by a web based questionnaire survey, and a workshop that connected the model developers and the users. For qualifying the stakeholder engagement, GEUS has selected a representative catchment area (Skjern River) for testing and demonstrating a prototype of the web based hydrological warning system at the workshop, and illustrated simulated groundwater levels, streamflow and water content in the root zone. The webpages can be tailor-made to meet the requirements of the end-users and also enable flexibility to extend while the users' demand changes. The active involvement of stakeholders in the workshop provided very valuable insights and feedbacks for GEUS, relevant for the future development of the nationwide real-time modeling and water cycle monitoring system for Denmark, including possible linking to early warning and real-time forecasting systems operating at the local scale.

Droughts in the US: Modeling and Forecasting for Agriculture-Water Management and Adaptation

NASA Astrophysics Data System (ADS)

Perveen, S.; Devineni, N.; Lall, U.

2012-12-01

More than half of all US counties are currently mired in a drought that is considered the worst in decades. A persistent drought can not only lead to widespread impacts on water access with interstate implications (as has been shown in the Southeast US and Texas), chronic scarcity can emerge as a risk in regions where fossil aquifers have become the primary source of supply and are being depleted at rates much faster than recharge (e.g., Midwestern US). The standardized drought indices on which the drought declarations are made in the US so far consider only the static decision frameworks—where only the supply is the control variable and not the water consumption. If a location has low demands, drought as manifest in the usual indices does not really have "proportionate" social impact. Conversely, a modest drought as indicated by the traditional measures may have significant impacts where demand is close to the climatological mean value of precipitation. This may also lead to drought being declared too late or too soon. Against this fact, the importance of improved drought forecasting and preparedness for different sectors of the economy becomes increasingly important. The central issue we propose to address through this paper is the construction and testing of a drought index that considers regional water demands for specific purposes (e.g., crops, municipal use) and their temporal distribution over the year for continental US. Here, we have highlighted the use of the proposed index for three main sectors: (i) water management organizations, (ii) optimizing agricultural water use, and (iii) supply chain water risk. The drought index will consider day-to-day climate variability and sectoral demands to develop aggregate regional conditions or disaggregated indices for water users. For the daily temperature and precipitation data, we are using NLDAS dataset that is available from 1949 onwards. The national agricultural statistics services (NASS) online database has been accessed for the agricultural data at the county level. Preliminary analyses show that large parts of Midwest and Southern parts of Florida and California are prone to multiyear droughts. This can primarily be attributed to high agricultural and/or urban water demands coupled with high interannual variability in supply. We propose to develop season-ahead and monthly updated forecasts of the drought index for informing the drought management plans. Given the already customized (sector specific) nature of the proposed drought index and its ability to represent the variability in both supply and demand, the early warning or forecasting of the index would not only complement the drought early warning systems in place by the national integrated drought information system (NIDIS) but also help in prescribing the ameliorative measures for adaptation.

[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

The Assessment of Climatological Impacts on Agricultural Production and Residential Energy Demand

NASA Astrophysics Data System (ADS)

Cooter, Ellen Jean

The assessment of climatological impacts on selected economic activities is presented as a multi-step, inter -disciplinary problem. The assessment process which is addressed explicitly in this report focuses on (1) user identification, (2) direct impact model selection, (3) methodological development, (4) product development and (5) product communication. Two user groups of major economic importance were selected for study; agriculture and gas utilities. The broad agricultural sector is further defined as U.S.A. corn production. The general category of utilities is narrowed to Oklahoma residential gas heating demand. The CERES physiological growth model was selected as the process model for corn production. The statistical analysis for corn production suggests that (1) although this is a statistically complex model, it can yield useful impact information, (2) as a result of output distributional biases, traditional statistical techniques are not adequate analytical tools, (3) the model yield distribution as a whole is probably non-Gausian, particularly in the tails and (4) there appears to be identifiable weekly patterns of forecasted yields throughout the growing season. Agricultural quantities developed include point yield impact estimates and distributional characteristics, geographic corn weather distributions, return period estimates, decision making criteria (confidence limits) and time series of indices. These products were communicated in economic terms through the use of a Bayesian decision example and an econometric model. The NBSLD energy load model was selected to represent residential gas heating consumption. A cursory statistical analysis suggests relationships among weather variables across the Oklahoma study sites. No linear trend in "technology -free" modeled energy demand or input weather variables which would correspond to that contained in observed state -level residential energy use was detected. It is suggested that this trend is largely the result of non-weather factors such as population and home usage patterns rather than regional climate change. Year-to-year changes in modeled residential heating demand on the order of 10('6) Btu's per household were determined and later related to state -level components of the Oklahoma economy. Products developed include the definition of regional forecast areas, likelihood estimates of extreme seasonal conditions and an energy/climate index. This information is communicated in economic terms through an input/output model which is used to estimate changes in Gross State Product and Household income attributable to weather variability.

Coupling Radar Rainfall to Hydrological Models for Water Abstraction Management

NASA Astrophysics Data System (ADS)

Asfaw, Alemayehu; Shucksmith, James; Smith, Andrea; MacDonald, Ken

2015-04-01

The impacts of climate change and growing water use are likely to put considerable pressure on water resources and the environment. In the UK, a reform to surface water abstraction policy has recently been proposed which aims to increase the efficiency of using available water resources whilst minimising impacts on the aquatic environment. Key aspects to this reform include the consideration of dynamic rather than static abstraction licensing as well as introducing water trading concepts. Dynamic licensing will permit varying levels of abstraction dependent on environmental conditions (i.e. river flow and quality). The practical implementation of an effective dynamic abstraction strategy requires suitable flow forecasting techniques to inform abstraction asset management. Potentially the predicted availability of water resources within a catchment can be coupled to predicted demand and current storage to inform a cost effective water resource management strategy which minimises environmental impacts. The aim of this work is to use a historical analysis of UK case study catchment to compare potential water resource availability using modelled dynamic abstraction scenario informed by a flow forecasting model, against observed abstraction under a conventional abstraction regime. The work also demonstrates the impacts of modelling uncertainties on the accuracy of predicted water availability over range of forecast lead times. The study utilised a conceptual rainfall-runoff model PDM - Probability-Distributed Model developed by Centre for Ecology & Hydrology - set up in the Dove River catchment (UK) using 1km2 resolution radar rainfall as inputs and 15 min resolution gauged flow data for calibration and validation. Data assimilation procedures are implemented to improve flow predictions using observed flow data. Uncertainties in the radar rainfall data used in the model are quantified using artificial statistical error model described by Gaussian distribution and propagated through the model to assess its influence on the forecasted flow uncertainty. Furthermore, the effects of uncertainties at different forecast lead times on potential abstraction strategies are assessed. The results show that over a 10 year period, an average of approximately 70 ML/d of potential water is missed in the study catchment under a convention abstraction regime. This indicates a considerable potential for the use of flow forecasting models to effectively implement advanced abstraction management and more efficiently utilize available water resources in the study catchment.

A review of tephra transport and dispersal models: Evolution, current status, and future perspectives

NASA Astrophysics Data System (ADS)

Folch, A.

2012-08-01

Tephra transport models try to predict atmospheric dispersion and sedimentation of tephra depending on meteorology, particle properties, and eruption characteristics, defined by eruption column height, mass eruption rate, and vertical distribution of mass. Models are used for different purposes, from operational forecast of volcanic ash clouds to hazard assessment of tephra dispersion and fallout. The size of the erupted particles, a key parameter controlling the dynamics of particle sedimentation in the atmosphere, varies within a wide range. Largest centimetric to millimetric particles fallout at proximal to medial distances from the volcano and sediment by gravitational settling. On the other extreme, smallest micrometric to sub-micrometric particles can be transported at continental or even at global scales and are affected by other deposition and aggregation mechanisms. Different scientific communities had traditionally modeled the dispersion of these two end members. Volcanologists developed families of models suitable for lapilli and coarse ash and aimed at computing fallout deposits and for hazard assessment. In contrast, meteorologists and atmospheric scientists have traditionally used other atmospheric transport models, dealing with finer particles, for tracking motion of volcanic ash clouds and, eventually, for computing airborne ash concentrations. During the last decade, the increasing demand for model accuracy and forecast reliability has pushed on two fronts. First, the original gap between these different families of models has been filled with the emergence of multi-scale and multi-purpose models. Second, new modeling strategies including, for example, ensemble and probabilistic forecast or model data assimilation are being investigated for future implementation in models and or modeling strategies. This paper reviews the evolution of tephra transport and dispersal models during the last two decades, presents the status and limitations of the current modeling strategies, and discusses some emergent perspectives expected to be implemented at operational level during the next few years. Improvements in both real-time forecasting and long-term hazard assessment are necessary to loss prevention programs on a local, regional, national and international level.

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

A Study of The Effect of Demand Uncertainty for Low-Carbon Products Using a Newsvendor Model

PubMed Central

Qu, Shaojian; Zhou, Yongyi

2017-01-01

This paper studies the effect of uncertain demand on a low-carbon product by using a newsvendor model. With two different kinds of market scales, we examine a game whereby a manufacturer produces and delivers a single new low-carbon product to a single retailer. The retailer observes the demand information and gives an order before the selling season. We find in the game that if the retailer shares truthful (or in contrast unreal or even does not share) forecast information with the manufacturer, the manufacturer will give a low (high) wholesale price through the sequence of events. In addition, as a policy-maker, the government posts a subsidy by selling the low-carbon product per unit. The manufacturer creates a new contract with a rebate for the retailer. We also take the consumer aversion coefficient and truth coefficient as qualitative variables into our model to study the order, pricing, and expected profit for the members of supply chain. The research shows that uncertain demand causes a the major effect on the new low-carbon product. Thereby, we suggest the retailer should share more truthful information with the manufacturer. PMID:29068382

A Study of The Effect of Demand Uncertainty for Low-Carbon Products Using a Newsvendor Model.

PubMed

Qu, Shaojian; Zhou, Yongyi

2017-10-25

This paper studies the effect of uncertain demand on a low-carbon product by using a newsvendor model. With two different kinds of market scales, we examine a game whereby a manufacturer produces and delivers a single new low-carbon product to a single retailer. The retailer observes the demand information and gives an order before the selling season. We find in the game that if the retailer shares truthful (or in contrast unreal or even does not share) forecast information with the manufacturer, the manufacturer will give a low (high) wholesale price through the sequence of events. In addition, as a policy-maker, the government posts a subsidy by selling the low-carbon product per unit. The manufacturer creates a new contract with a rebate for the retailer. We also take the consumer aversion coefficient and truth coefficient as qualitative variables into our model to study the order, pricing, and expected profit for the members of supply chain. The research shows that uncertain demand causes a the major effect on the new low-carbon product. Thereby, we suggest the retailer should share more truthful information with the manufacturer.

United States Registered Nurse Workforce Report Card and Shortage Forecast: A Revisit.

PubMed

Zhang, Xiaoming; Tai, Daniel; Pforsich, Hugh; Lin, Vernon W

This is a reevaluation of registered nurse (RN) supply and demand from 2016 to 2030 using a previously published work forecast model and grading methodology with more recent workforce data. There will be a shortage of 154 018 RNs by 2020 and 510 394 RNs by 2030; the South and West regions will have higher shortage ratios than Northeast and Midwest regions. This reflects a nearly 50% overall improvement when compared with the authors' prior study, and the low-performing states have improved from 18 "D" and 12 "F" grades as published earlier to 13 "D" and 1 "F" in this study. Although progress has been made, efforts to foster the pipelines for improving the nursing workforce need to be continued.

Statistical control in hydrologic forecasting.

Treesearch

H.G. Wilm

1950-01-01

With rapidly growing development and uses of water, a correspondingly great demand has developed for advance estimates of the volumes or rates of flow which are supplied by streams. Therefore much attention is being devoted to hydrologic forecasting, and numerous methods have been tested in efforts to make increasingly reliable estimates of future supplies.

Forecast Occupational Supply: A Methodological Handbook.

ERIC Educational Resources Information Center

McKinlay, Bruce; Johnson, Lowell E.

Greater concern with unemployment in recent years has increased the need for accurate forecasting of future labor market requirements, in order to plan for vocational education and other manpower programs. However, past emphasis has been placed on labor demand, rather than supply, even though either side by itself is useless in determining skill…

FORECASTING AIR QUALITY OVER THE UNITED STATES

EPA Science Inventory

Increased awareness of national air quality issues on the part of the media and the general public have recently led to more demand for short-term (1-2 day) air quality forecasts for use in assessing potential health impacts (e.g., on children, the elderly, and asthmatics) and po...

Socioeconomic and travel demand forecasts for Virginia and potential policy responses : a report for VTrans2035 : Virginia's statewide multimodal transportation plan.

DOT National Transportation Integrated Search

2009-01-01

VTrans2035, Virginia's statewide multimodal transportation plan, requires 25-year forecasts of socioeconomic and travel activity. Between 2010 and 2035, daily vehicle miles traveled (DVMT) will increase between 35% and 45%, accompanied by increases i...

Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch

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

Zhang, Yingchen; Yang, Rui; Jiang, Huaiguang

For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methodsmore » to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.« less

Telecommunication service markets through the year 2000 in relation to millimeter wave satellite systems

NASA Technical Reports Server (NTRS)

Stevenson, S. M.

1979-01-01

NASA is currently conducting a series of millimeter wave satellite system market studies to develop 30/20 GHz satellite system concepts that have commercial potential. Four contractual efforts were undertaken: two parallel and independent system studies and two parallel and independent market studies. The marketing efforts are focused on forecasting the total domestic demand for long haul telecommunications services for the 1980-2000 period. Work completed to date and reported in this paper include projections of: geographical distribution of traffic; traffic volume as a function of urban area size; and user identification and forecasted demand.

NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts

NASA Technical Reports Server (NTRS)

Viken, Jeff; Dollyhigh, Samuel; Smith, Jeremy; Trani, Antonio; Baik, Hojong; Hinze, Nicholas; Ashiabor, Senanu

2006-01-01

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

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.

Applying linear programming model to aggregate production planning of coated peanut products

NASA Astrophysics Data System (ADS)

Rohmah, W. G.; Purwaningsih, I.; Santoso, EF S. M.

2018-03-01

The aim of this study was to set the overall production level for each grade of coated peanut product to meet market demands with a minimum production cost. The linear programming model was applied in this study. The proposed model was used to minimize the total production cost based on the limited demand of coated peanuts. The demand values applied to the method was previously forecasted using time series method and production capacity aimed to plan the aggregate production for the next 6 month period. The results indicated that the production planning using the proposed model has resulted a better fitted pattern to the customer demands compared to that of the company policy. The production capacity of product family A, B, and C was relatively stable for the first 3 months of the planning periods, then began to fluctuate over the next 3 months. While, the production capacity of product family D and E was fluctuated over the 6-month planning periods, with the values in the range of 10,864 - 32,580 kg and 255 – 5,069 kg, respectively. The total production cost for all products was 27.06% lower than the production cost calculated using the company’s policy-based method.

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.

Survival Sales Forecasting.

ERIC Educational Resources Information Center

Paradiso, James; Stair, Kenneth

Intended to provide insight into the dynamics of demand analysis, this paper presents an eight-step method for forecasting sales. Focusing on sales levels that must be achieved to enjoy targeted profits in favor of the usual approach of emphasizing how much will be sold within a given period, a sample situation is provided to illustrate this…

Matching Community and Technical College Professional/Technical Education Capacity to Employer Demand. Final Report.

ERIC Educational Resources Information Center

Sommers, Paul; Heg, Deena

A project was conducted to improve the state of Washington's community and technical college system by developing and using an improved occupational forecasting system to assess and respond to education and training needs. First, long-term occupational forecast data from Washington's Employment Security Department were matched with technical and…

Relationship of Solar Energy Installation Permits to Renewable Portfolio Standards and Insolation

NASA Astrophysics Data System (ADS)

Butler, Kirt Gordon

Legislated renewable portfolio standards (RPSs) may not be the key to ensure forecast energy demands are met. States without a legislated RPS and with efficient permitting procedures were found to have approved and issued 28.57% more permits on average than those with a legislated RPS. Assessment models to make informed decisions about the need and effect of legislated RPSs do not exist. Decision makers and policy creators need to use empirical data and a viable model to resolve the debate over a nationally legislated RPS. The purpose of this cross-sectional study was to determine if relationships between the independent variables of RPS and insolation levels and the dependent variable of the percentage of permits approved would prove to be a viable model. The research population was 68 cities in the United States, of which 55 were used in this study. The return on investment economic decision model provided the theoretical framework for this study and the model generated. The output of multiple regression analysis indicated a weak to medium positive relationship among the variables. None of these relationships were statistically significant at the 0.05 level. A model using site specific data might yield significant results and be useful for determining which solar energy projects to pursue and where to implement them without Federal or State mandated RPSs. A viable model would bring about efficiency gains in the permitting process and effectiveness gains in promoting installations of solar energy-based systems. Research leading to the development of a viable model would benefit society by encouraging the development of sustainable energy sources and helping to meet forecast energy demands.

Using HPC within an operational forecasting configuration

NASA Astrophysics Data System (ADS)

Jagers, H. R. A.; Genseberger, M.; van den Broek, M. A. F. H.

2012-04-01

Various natural disasters are caused by high-intensity events, for example: extreme rainfall can in a short time cause major damage in river catchments, storms can cause havoc in coastal areas. To assist emergency response teams in operational decisions, it's important to have reliable information and predictions as soon as possible. This starts before the event by providing early warnings about imminent risks and estimated probabilities of possible scenarios. In the context of various applications worldwide, Deltares has developed an open and highly configurable forecasting and early warning system: Delft-FEWS. Finding the right balance between simulation time (and hence prediction lead time) and simulation accuracy and detail is challenging. Model resolution may be crucial to capture certain critical physical processes. Uncertainty in forcing conditions may require running large ensembles of models; data assimilation techniques may require additional ensembles and repeated simulations. The computational demand is steadily increasing and data streams become bigger. Using HPC resources is a logical step; in different settings Delft-FEWS has been configured to take advantage of distributed computational resources available to improve and accelerate the forecasting process (e.g. Montanari et al, 2006). We will illustrate the system by means of a couple of practical applications including the real-time dynamic forecasting of wind driven waves, flow of water, and wave overtopping at dikes of Lake IJssel and neighboring lakes in the center of The Netherlands. Montanari et al., 2006. Development of an ensemble flood forecasting system for the Po river basin, First MAP D-PHASE Scientific Meeting, 6-8 November 2006, Vienna, Austria.

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

Raingauge-Based Rainfall Nowcasting with Artificial Neural Network

NASA Astrophysics Data System (ADS)

Liong, Shie-Yui; He, Shan

2010-05-01

Rainfall forecasting and nowcasting are of great importance, for instance, in real-time flood early warning systems. Long term rainfall forecasting demands global climate, land, and sea data, thus, large computing power and storage capacity are required. Rainfall nowcasting's computing requirement, on the other hand, is much less. Rainfall nowcasting may use data captured by radar and/or weather stations. This paper presents the application of Artificial Neural Network (ANN) on rainfall nowcasting using data observed at weather and/or rainfall stations. The study focuses on the North-East monsoon period (December, January and February) in Singapore. Rainfall and weather data from ten stations, between 2000 and 2006, were selected and divided into three groups for training, over-fitting test and validation of the ANN. Several neural network architectures were tried in the study. Two architectures, Backpropagation ANN and Group Method of Data Handling ANN, yielded better rainfall nowcasting, up to two hours, than the other architectures. The obtained rainfall nowcasts were then used by a catchment model to forecast catchment runoff. The results of runoff forecast are encouraging and promising.With ANN's high computational speed, the proposed approach may be deliverable for creating the real-time flood early warning system.

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

NASA Astrophysics Data System (ADS)

Efremenko, Vladimir; Belyaevsky, Roman; Skrebneva, Evgeniya

2017-11-01

In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.

Short-term forecasting of turbidity in trunk main networks.

PubMed

Meyers, Gregory; Kapelan, Zoran; Keedwell, Edward

2017-11-01

Water discolouration is an increasingly important and expensive issue due to rising customer expectations, tighter regulatory demands and ageing Water Distribution Systems (WDSs) in the UK and abroad. This paper presents a new turbidity forecasting methodology capable of aiding operational staff and enabling proactive management strategies. The turbidity forecasting methodology developed here is completely data-driven and does not require hydraulic or water quality network model that is expensive to build and maintain. The methodology is tested and verified on a real trunk main network with observed turbidity measurement data. Results obtained show that the methodology can detect if discolouration material is mobilised, estimate if sufficient turbidity will be generated to exceed a preselected threshold and approximate how long the material will take to reach the downstream meter. Classification based forecasts of turbidity can be reliably made up to 5 h ahead although at the expense of increased false alarm rates. The methodology presented here could be used as an early warning system that can enable a multitude of cost beneficial proactive management strategies to be implemented as an alternative to expensive trunk mains cleaning programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Application of Magnesium Alloys in Aircraft Interiors — Changing the Rules

NASA Astrophysics Data System (ADS)

Davis, Bruce

The commercial aircraft market is forecast to steadily grow over the next two decades. Part of this growth is driven by the desire of airlines to replace older models in their fleet with newer, more fuel efficient designs, to realize lower operating costs and to address the rising cost of aviation fuel. As such the aircraft OEMs are beginning to set more and more demanding mass targets on their new platforms.

Effective Capital Provision Within Government. Methodologies for Right-Sizing Base Infrastructure

DTIC Science & Technology

2005-01-01

unknown distributions, since they more accurately represent the complexity of real -world problems. Forecasting uncertain future demand flows is critical to...ordering system with no time lags and no additional costs for instantaneous delivery, shortage and holding costs would be eliminated, because the...order a fixed quantity, Q. 4.1.4 Analyzed Time Step Time is an important dimension in inventory models, since the way the system changes over time affects

Value of Adaptive Drought Forecasting and Management for the ACF River Basin in the Southeast U.S.

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.; Kistenmacher, M.

2016-12-01

In recent times, severe droughts in the southeast U.S. occur every 6 to 10 years and last for up to 4 years. During such drought episodes, the ACF River Basin supplies decline by up to 50 % of their normal levels, and water stresses increase rather markedly, exacerbating stakeholder anxiety and conflicts. As part of the ACF Stakeholder planning process, GWRI has developed new tools and carried out comprehensive assessments to provide quantitative answers to several important questions related to drought prediction and management: (i) Can dry and wet climatic periods be reliably anticipated with sufficiently long lead times? What drought indices can support reliable, skillful, and long-lead forecasts? (ii) What management objectives can seasonal climate forecasts benefit? How should benefits/impacts be shared? (iii) What operational adjustments are likely to mitigate stakeholder impacts or increase benefits consistent with stakeholder expectations? Regarding drought prediction, a large number of indices were defined and tested at different basin locations and lag times. These included local/cumulative unimpaired flows (UIFs) at 10 river nodes; Mean Areal Precipitation (MAP); Standard Precipitation Index (SPI); Palmer Drought Severity Index; Palmer Modified Drought Index; Palmer Z-Index; Palmer Hydrologic Drought Severity Index; and Soil Moisture—GWRI watershed model. Our findings show that all ACF sub-basins exhibit good forecast skill throughout the year and with sufficient lead time. Index variables with high explanatory value include: previous UIFs, soil moisture states (generated by the GWRI watershed model), and PDSI. Regarding drought management, assessments with coupled forecast-management schemes demonstrate that the use of adaptive forecast-management procedures improves reservoir operations and meets basin demands more reliably. Such improvements can support better management of lake levels, higher environmental and navigation flows, higher dependable power generation hours, and better management of consumptive uses without adverse impacts on other stakeholder interests. However, realizing these improvements requires (1) usage of adaptive reservoir management procedures (incorporating forecasts), and (2) stakeholder agreement on equitable benefit sharing.

Market capture by 30/20 GHz satellite systems, volume 2

NASA Technical Reports Server (NTRS)

Gamble, R. B.; Saporta, L.

1981-01-01

Results of a telecommunications demand study are presented. Forecasts of demand for 30/20 GHz satellite systems, and the expected build up of traffic on these systems are given as a function of time for each of several operational scenarios.

Market capture by 30/20 GHz satellite systems, volume 2

NASA Astrophysics Data System (ADS)

Gamble, R. B.; Saporta, L.

1981-04-01

Results of a telecommunications demand study are presented. Forecasts of demand for 30/20 GHz satellite systems, and the expected build up of traffic on these systems are given as a function of time for each of several operational scenarios.

A new solar power output prediction based on hybrid forecast engine and decomposition model.

PubMed

Zhang, Weijiang; Dang, Hongshe; Simoes, Rolando

2018-06-12

Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Communications to 2000 - A forecast of the demand and capacity of U.S. domestic communications satellites

NASA Astrophysics Data System (ADS)

Fordyce, S. W.

The market demand for the U.S. domestic communications satellites accelerated in the late 70's, exceeding the capacity of the satellites currently in orbit. Satellite carriers have been authorized to build an additional 24 domsats. This paper examines the anticipated market demands, and the capability of the domsats to fulfill these demands. With various practical technical innovations, the domsats appear able to meet the expected market demands until the end of this century.

Adaptive heat pump and battery storage demand side energy management

NASA Astrophysics Data System (ADS)

Sobieczky, Florian; Lettner, Christian; Natschläger, Thomas; Traxler, Patrick

2017-11-01

An adaptive linear model predictive control strategy is introduced for the problem of demand side energy management, involving a photovoltaic device, a battery, and a heat pump. Moreover, the heating influence of solar radiation via the glass house effect is considered. Global sunlight radiation intensity and the outside temperature are updated by weather forecast data. The identification is carried out after adapting to a time frame witch sufficiently homogeneous weather. In this way, in spite of the linearity an increase in precision and cost reduction of up to 46% is achieved. It is validated for an open and closed loop version of the MPC problem using real data of the ambient temperature and the global radiation.

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak; Wang, Yao Xun; Sridhar, Banavar

2013-01-01

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

Forecasting of indirect consumables for a Job Shop

NASA Astrophysics Data System (ADS)

Shakeel, M.; Khan, S.; Khan, W. A.

2016-08-01

A job shop has an arrangement where similar machines (Direct consumables) are grouped together and use indirect consumables to produce a product. The indirect consumables include hack saw blades, emery paper, painting brush etc. The job shop is serving various orders at a particular time for the optimal operation of job shop. Forecasting is required to predict the demand of direct and indirect consumables in a job shop. Forecasting is also needed to manage lead time, optimize inventory cost and stock outs. The objective of this research is to obtain the forecast for indirect consumables. The paper shows how job shop can manage their indirect consumables more accurately by establishing a new technique of forecasting. This results in profitable use of job shop by multiple users.

Consequences Identification in Forecasting and Ethical Decision-making

PubMed Central

Stenmark, Cheryl K.; Antes, Alison L.; Thiel, Chase E.; Caughron, Jared J.; Wang, Xiaoqian; Mumford, Michael D.

2015-01-01

Forecasting involves predicting outcomes based on observations of the situation at hand. We examined the impact of the number and types of consequences considered on the quality of ethical decision-making. Undergraduates role-played several ethical problems in which they forecast potential outcomes and made decisions. Performance pressure (difficult demands placed on the situation) and interpersonal conflict (clashes among people in the problem situation) were manipulated within each problem scenario. The results indicated that the identification of potential consequences was positively associated with both higher quality forecasts and more ethical decisions. Neither performance pressure nor interpersonal conflict affected the quality of forecasts or decisions. Theoretical and practical implications of these findings and the use of this research approach are discussed. PMID:21460584

Extended-range forecasting of Chinese summer surface air temperature and heat waves

NASA Astrophysics Data System (ADS)

Zhu, Zhiwei; Li, Tim

2018-03-01

Because of growing demand from agricultural planning, power management and activity scheduling, extended-range (5-30-day lead) forecasting of summer surface air temperature (SAT) and heat waves over China is carried out in the present study via spatial-temporal projection models (STPMs). Based on the training data during 1960-1999, the predictability sources are found to propagate from Europe, Northeast Asia, and the tropical Pacific, to influence the intraseasonal 10-80 day SAT over China. STPMs are therefore constructed using the projection domains, which are determined by these previous predictability sources. For the independent forecast period (2000-2013), the STPMs can reproduce EOF-filtered 30-80 day SAT at all lead times of 5-30 days over most part of China, and observed 30-80 and 10-80 day SAT at 25-30 days over eastern China. Significant pattern correlation coefficients account for more than 50% of total forecasts at all 5-30-day lead times against EOF-filtered and observed 30-80 day SAT, and at a 20-day lead time against observed 10-80 day SAT. The STPMs perform poorly in reproducing 10-30 day SAT. Forecasting for the first two modes of 10-30 day SAT only shows useful skill within a 15-day lead time. Forecasting for the third mode of 10-30 day SAT is useless after a 10-day lead time. The forecasted heat waves over China are determined by the reconstructed SAT which is the summation of the forecasted 10-80 day SAT and the lower frequency (longer than 80-day) climatological SAT. Over a large part of China, the STPMs can forecast more than 30% of heat waves within a 15-day lead time. In general, the STPMs demonstrate the promising skill for extended-range forecasting of Chinese summer SAT and heat waves.

Essays on U.S. energy markets

NASA Astrophysics Data System (ADS)

Brightwell, David A.

2008-04-01

This dissertation examines three facets of U.S. energy use and policy. First, I examine the Gulf Coast petroleum refining industry to determine the structure of the industry. Using the duality between cost-minimization and production functions, I estimate the demand for labor to determine the underlying production function. The results indicate that refineries have become more capital intensive due to the relative price increase of labor. The industry has consolidated in response to higher labor costs and costs of environmental compliance. Next, I examine oil production in the United States. An empirical model based on the theoretical framework of Pindyck is used to estimate production. This model differs from previous research by using state level data rather than national level data. The results indicate that the production elasticity with respect to reserves and the price elasticity of supply are both inelastic in the long run. The implication of these findings is that policies designed to increase domestic production through subsidies, tax breaks, or royalty reductions will likely provide little additional oil. We simulate production under three scenarios. In the most extreme scenario, prices double between 2005 and 2030 while reserves increase by 50%. Under this scenario, oil production in 2030 is approximately the same as the 2005 level. The third essay estimates demand for fossil fuels in the U.S. and uses these estimates to forecast CO2 emissions. The results indicate that there is almost no substitution from one fossil fuel to another and that all three fossil fuels are inelastic in the long run. Additionally, all three fuels respond differently to changes in GDP. The result of the differing elasticities with respect to GDP is that the energy mix has changed over time. The implication for forecasting CO2 emissions is that models that cannot distinguish changes in the energy mix are not effective in forecasting CO2 emissions.

Fuzzy Multi-Objective Transportation Planning with Modified S-Curve Membership Function

NASA Astrophysics Data System (ADS)

Peidro, D.; Vasant, P.

2009-08-01

In this paper, the S-Curve membership function methodology is used in a transportation planning decision (TPD) problem. An interactive method for solving multi-objective TPD problems with fuzzy goals, available supply and forecast demand is developed. The proposed method attempts simultaneously to minimize the total production and transportation costs and the total delivery time with reference to budget constraints and available supply, machine capacities at each source, as well as forecast demand and warehouse space constraints at each destination. We compare in an industrial case the performance of S-curve membership functions, representing uncertainty goals and constraints in TPD problems, with linear membership functions.

Developing guidelines for incorporating managing demand into WSDOT planning and programming: transportation demand management guidance for corridor planning studies.

DOT National Transportation Integrated Search

2016-02-01

The Washington State Department of Transportation (WSDOT) regional planning programs address current and forecasted deficiencies of State highways through the conduct of corridor studies. This Guidance for the conduct of corridor planning studies is ...

Future Skill Needs in Europe: Critical Labour Force Trends. Cedefop Research Paper. No 59

ERIC Educational Resources Information Center

Cedefop - European Centre for the Development of Vocational Training, 2016

2016-01-01

The European labour market is challenged by changes in the demographic composition of the labour force and increasing work complexities and processes. Skills forecasting makes useful contribution to decisions by policy-makers, experts and individuals. In this publication, Cedefop presents the latest results of skills supply and demand forecasts.…

The Labor Market and Illegal Immigration: The Outlook for the 1980s.

ERIC Educational Resources Information Center

Wachter, Michael L.

1980-01-01

A labor supply forecast is developed for the U.S. labor market in the 1980s, focusing on the effects of the low fertility rates of recent years. That forecast is then compared with the Bureau of Labor Statistics projection of employment demand in the next decade. Effects of illegal immigrants are also discussed. (CT)

Reliable estimates of predictive uncertainty for an Alpine catchment using a non-parametric methodology

NASA Astrophysics Data System (ADS)

Matos, José P.; Schaefli, Bettina; Schleiss, Anton J.

2017-04-01

Uncertainty affects hydrological modelling efforts from the very measurements (or forecasts) that serve as inputs to the more or less inaccurate predictions that are produced. Uncertainty is truly inescapable in hydrology and yet, due to the theoretical and technical hurdles associated with its quantification, it is at times still neglected or estimated only qualitatively. In recent years the scientific community has made a significant effort towards quantifying this hydrologic prediction uncertainty. Despite this, most of the developed methodologies can be computationally demanding, are complex from a theoretical point of view, require substantial expertise to be employed, and are constrained by a number of assumptions about the model error distribution. These assumptions limit the reliability of many methods in case of errors that show particular cases of non-normality, heteroscedasticity, or autocorrelation. The present contribution builds on a non-parametric data-driven approach that was developed for uncertainty quantification in operational (real-time) forecasting settings. The approach is based on the concept of Pareto optimality and can be used as a standalone forecasting tool or as a postprocessor. By virtue of its non-parametric nature and a general operating principle, it can be applied directly and with ease to predictions of streamflow, water stage, or even accumulated runoff. Also, it is a methodology capable of coping with high heteroscedasticity and seasonal hydrological regimes (e.g. snowmelt and rainfall driven events in the same catchment). Finally, the training and operation of the model are very fast, making it a tool particularly adapted to operational use. To illustrate its practical use, the uncertainty quantification method is coupled with a process-based hydrological model to produce statistically reliable forecasts for an Alpine catchment located in Switzerland. Results are presented and discussed in terms of their reliability and resolution.

Assessment of Global Forecast Ocean Assimilation Model (FOAM) using new satellite SST data

NASA Astrophysics Data System (ADS)

Ascione Kenov, Isabella; Sykes, Peter; Fiedler, Emma; McConnell, Niall; Ryan, Andrew; Maksymczuk, Jan

2016-04-01

There is an increased demand for accurate ocean weather information for applications in the field of marine safety and navigation, water quality, offshore commercial operations, monitoring of oil spills and pollutants, among others. The Met Office, UK, provides ocean forecasts to customers from governmental, commercial and ecological sectors using the Global Forecast Ocean Assimilation Model (FOAM), an operational modelling system which covers the global ocean and runs daily, using the NEMO (Nucleus for European Modelling of the Ocean) ocean model with horizontal resolution of 1/4° and 75 vertical levels. The system assimilates salinity and temperature profiles, sea surface temperature (SST), sea surface height (SSH), and sea ice concentration observations on a daily basis. In this study, the FOAM system is updated to assimilate Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) SST data. Model results from one month trials are assessed against observations using verification tools which provide a quantitative description of model performance and error, based on statistical metrics, including mean error, root mean square error (RMSE), correlation coefficient, and Taylor diagrams. A series of hindcast experiments is used to run the FOAM system with AMSR2 and SEVIRI SST data, using a control run for comparison. Results show that all trials perform well on the global ocean and that largest SST mean errors were found in the Southern hemisphere. The geographic distribution of the model error for SST and temperature profiles are discussed using statistical metrics evaluated over sub-regions of the global ocean.

Predicting coastal cliff erosion using a Bayesian probabilistic model

USGS Publications Warehouse

Hapke, Cheryl J.; Plant, Nathaniel G.

2010-01-01

Regional coastal cliff retreat is difficult to model due to the episodic nature of failures and the along-shore variability of retreat events. There is a growing demand, however, for predictive models that can be used to forecast areas vulnerable to coastal erosion hazards. Increasingly, probabilistic models are being employed that require data sets of high temporal density to define the joint probability density function that relates forcing variables (e.g. wave conditions) and initial conditions (e.g. cliff geometry) to erosion events. In this study we use a multi-parameter Bayesian network to investigate correlations between key variables that control and influence variations in cliff retreat processes. The network uses Bayesian statistical methods to estimate event probabilities using existing observations. Within this framework, we forecast the spatial distribution of cliff retreat along two stretches of cliffed coast in Southern California. The input parameters are the height and slope of the cliff, a descriptor of material strength based on the dominant cliff-forming lithology, and the long-term cliff erosion rate that represents prior behavior. The model is forced using predicted wave impact hours. Results demonstrate that the Bayesian approach is well-suited to the forward modeling of coastal cliff retreat, with the correct outcomes forecast in 70–90% of the modeled transects. The model also performs well in identifying specific locations of high cliff erosion, thus providing a foundation for hazard mapping. This approach can be employed to predict cliff erosion at time-scales ranging from storm events to the impacts of sea-level rise at the century-scale.

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

2015-01-01

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity-building applications that extend numerical weather prediction to developing countries are intended to provide near real-time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather-related hazards, or impacts that local weather events may have during the recovery phase.

An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand.

PubMed

Gupta, Saurabh; Black-Schaffer, W Stephen; Crawford, James M; Gross, David; Karcher, Donald S; Kaufman, Jill; Knapman, Doug; Prystowsky, Michael B; Wheeler, Thomas M; Bean, Sarah; Kumar, Paramhans; Sharma, Raghav; Chamoli, Vaibhav; Ghai, Vikrant; Gogia, Vineet; Weintraub, Sally; Cohen, Michael B; Robboy, Stanley J

2015-01-01

Effective physician workforce management requires that the various organizations comprising the House of Medicine be able to assess their current and future workforce supply. This information has direct relevance to funding of graduate medical education. We describe a dynamic modeling tool that examines how individual factors and practice variables can be used to measure and forecast the supply and demand for existing and new physician services. The system we describe, while built to analyze the pathologist workforce, is sufficiently broad and robust for use in any medical specialty. Our design provides a computer-based software model populated with data from surveys and best estimates by specialty experts about current and new activities in the scope of practice. The model describes the steps needed and data required for analysis of supply and demand. Our modeling tool allows educators and policy makers, in addition to physician specialty organizations, to assess how various factors may affect demand (and supply) of current and emerging services. Examples of factors evaluated include types of professional services (3 categories with 16 subcategories), service locations, elements related to the Patient Protection and Affordable Care Act, new technologies, aging population, and changing roles in capitated, value-based, and team-based systems of care. The model also helps identify where physicians in a given specialty will likely need to assume new roles, develop new expertise, and become more efficient in practice to accommodate new value-based payment models.

An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand

PubMed Central

Gupta, Saurabh; Black-Schaffer, W. Stephen; Crawford, James M.; Gross, David; Karcher, Donald S.; Kaufman, Jill; Knapman, Doug; Prystowsky, Michael B.; Wheeler, Thomas M.; Bean, Sarah; Kumar, Paramhans; Sharma, Raghav; Chamoli, Vaibhav; Ghai, Vikrant; Gogia, Vineet; Weintraub, Sally; Cohen, Michael B.

2015-01-01

Effective physician workforce management requires that the various organizations comprising the House of Medicine be able to assess their current and future workforce supply. This information has direct relevance to funding of graduate medical education. We describe a dynamic modeling tool that examines how individual factors and practice variables can be used to measure and forecast the supply and demand for existing and new physician services. The system we describe, while built to analyze the pathologist workforce, is sufficiently broad and robust for use in any medical specialty. Our design provides a computer-based software model populated with data from surveys and best estimates by specialty experts about current and new activities in the scope of practice. The model describes the steps needed and data required for analysis of supply and demand. Our modeling tool allows educators and policy makers, in addition to physician specialty organizations, to assess how various factors may affect demand (and supply) of current and emerging services. Examples of factors evaluated include types of professional services (3 categories with 16 subcategories), service locations, elements related to the Patient Protection and Affordable Care Act, new technologies, aging population, and changing roles in capitated, value-based, and team-based systems of care. The model also helps identify where physicians in a given specialty will likely need to assume new roles, develop new expertise, and become more efficient in practice to accommodate new value-based payment models. PMID:28725751

A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand

NASA Astrophysics Data System (ADS)

Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin

2013-07-01

The five-day biochemical oxygen demand (BOD5) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD5. In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d-factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.

Operational Hydrologic Forecasts in the Columbia River Basin

NASA Astrophysics Data System (ADS)

Shrestha, K. Y.; Curry, J. A.; Webster, P. J.; Toma, V. E.; Jelinek, M.

2013-12-01

The Columbia River Basin (CRB) covers an area of ~670,000 km2 and stretches across parts of seven U.S. states and one Canadian province. The basin is subject to a variable climate, and moisture stored in snowpack during the winter is typically released in spring and early summer. These releases contribute to rapid increases in flow. A number of impoundments have been constructed on the Columbia River main stem and its tributaries for the purposes of flood control, navigation, irrigation, recreation, and hydropower. Storage reservoirs allow water managers to adjust natural flow patterns to benefit water and energy demands. In the past decade, the complexity of water resource management issues in the basin has amplified the importance of streamflow forecasting. Medium-range (1-10 day) numerical weather forecasts of precipitation and temperature can be used to drive hydrological models. In this work, probabilistic meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) are used to force the Variable Infiltration Capacity (VIC) model. Soil textures were obtained from FAO data; vegetation types / land cover information from UMD land cover data; stream networks from USGS HYDRO1k; and elevations from CGIAR version 4 SRTM data. The surface energy balance in 0.25° (~25 km) cells is closed through an iterative process operating at a 6 hour timestep. Output fluxes from a number of cells in the basin are combined through one-dimensional flow routing predicated on assumptions of linearity and time invariance. These combinations lead to daily mean streamflow estimates at key locations throughout the basin. This framework is suitable for ingesting daily numerical weather prediction data, and was calibrated using USGS mean daily streamflow data at the Dalles Dam (TDA). Operational streamflow forecasts in the CRB have been active since October 2012. These are 'naturalized' or unregulated forecasts. In 2013, increases of ~2600 m3/s (~48% of average discharge for water years 1879-2012) or greater were observed at TDA during the following periods: 29 March to 12 April, 5 May to 11 May, and 19 June to 29 June. Precipitation and temperature forecasts during these periods are shown along with changes in the model simulated snowpack. We evaluate the performance of the ensemble mean 10 days in advance of each of these three events, and comment on how the distribution of ensemble members affected forecast confidence in each situation.

VISIR-I: small vessels, least-time nautical routes using wave forecasts

NASA Astrophysics Data System (ADS)

Mannarini, G.; Pinardi, N.; Coppini, G.; Oddo, P.; Iafrati, A.

2015-09-01

A new numerical model for the on-demand computation of optimal ship routes based on sea-state forecasts has been developed. The model, named VISIR (discoVerIng Safe and effIcient Routes) is designed to support decision-makers when planning a marine voyage. The first version of the system, VISIR-I, considers medium and small motor vessels with lengths of up to a few tens of meters and a displacement hull. The model is made up of three components: the route optimization algorithm, the mechanical model of the ship, and the environmental fields. The optimization algorithm is based on a graph-search method with time-dependent edge weights. The algorithm is also able to compute a voluntary ship speed reduction. The ship model accounts for calm water and added wave resistance by making use of just the principal particulars of the vessel as input parameters. The system also checks the optimal route for parametric roll, pure loss of stability, and surfriding/broaching-to hazard conditions. Significant wave height, wave spectrum peak period, and wave direction forecast fields are employed as an input. Examples of VISIR-I routes in the Mediterranean Sea are provided. The optimal route may be longer in terms of miles sailed and yet it is faster and safer than the geodetic route between the same departure and arrival locations. Route diversions result from the safety constraints and the fact that the algorithm takes into account the full temporal evolution and spatial variability of the environmental fields.

Predictive ecology: systems approaches

PubMed Central

Evans, Matthew R.; Norris, Ken J.; Benton, Tim G.

2012-01-01

The world is experiencing significant, largely anthropogenically induced, environmental change. This will impact on the biological world and we need to be able to forecast its effects. In order to produce such forecasts, ecology needs to become more predictive—to develop the ability to understand how ecological systems will behave in future, changed, conditions. Further development of process-based models is required to allow such predictions to be made. Critical to the development of such models will be achieving a balance between the brute-force approach that naively attempts to include everything, and over simplification that throws out important heterogeneities at various levels. Central to this will be the recognition that individuals are the elementary particles of all ecological systems. As such it will be necessary to understand the effect of evolution on ecological systems, particularly when exposed to environmental change. However, insights from evolutionary biology will help the development of models even when data may be sparse. Process-based models are more common, and are used for forecasting, in other disciplines, e.g. climatology and molecular systems biology. Tools and techniques developed in these endeavours can be appropriated into ecological modelling, but it will also be necessary to develop the science of ecoinformatics along with approaches specific to ecological problems. The impetus for this effort should come from the demand coming from society to understand the effects of environmental change on the world and what might be performed to mitigate or adapt to them. PMID:22144379

Hierarchical prediction of industrial water demand based on refined Laspeyres decomposition analysis.

PubMed

Shang, Yizi; Lu, Shibao; Gong, Jiaguo; Shang, Ling; Li, Xiaofei; Wei, Yongping; Shi, Hongwang

2017-12-01

A recent study decomposed the changes in industrial water use into three hierarchies (output, technology, and structure) using a refined Laspeyres decomposition model, and found monotonous and exclusive trends in the output and technology hierarchies. Based on that research, this study proposes a hierarchical prediction approach to forecast future industrial water demand. Three water demand scenarios (high, medium, and low) were then established based on potential future industrial structural adjustments, and used to predict water demand for the structural hierarchy. The predictive results of this approach were compared with results from a grey prediction model (GPM (1, 1)). The comparison shows that the results of the two approaches were basically identical, differing by less than 10%. Taking Tianjin, China, as a case, and using data from 2003-2012, this study predicts that industrial water demand will continuously increase, reaching 580 million m 3 , 776.4 million m 3 , and approximately 1.09 billion m 3 by the years 2015, 2020 and 2025 respectively. It is concluded that Tianjin will soon face another water crisis if no immediate measures are taken. This study recommends that Tianjin adjust its industrial structure with water savings as the main objective, and actively seek new sources of water to increase its supply.

Forecasting the demand potential for STOL air transportation

NASA Technical Reports Server (NTRS)

Fan, S.; Horonjeff, R.; Kanafani, A.; Mogharabi, A.

1973-01-01

A process for predicting the potential demand for STOL aircraft was investigated to provide a conceptual framework, and an analytical methodology for estimating the STOL air transportation market. It was found that: (1) schedule frequency has the strongest effect on the traveler's choice among available routes, (2) work related business constitutes approximately 50% of total travel volume, and (3) air travel demand follows economic trends.

Hourly Wind Speed Interval Prediction in Arid Regions

NASA Astrophysics Data System (ADS)

Chaouch, M.; Ouarda, T.

2013-12-01

The long and extended warm and dry summers, the low rate of rain and humidity are the main factors that explain the increase of electricity consumption in hot arid regions. In such regions, the ventilating and air-conditioning installations, that are typically the most energy-intensive among energy consumption activities, are essential for securing healthy, safe and suitable indoor thermal conditions for building occupants and stored materials. The use of renewable energy resources such as solar and wind represents one of the most relevant solutions to overcome the increase of the electricity demand challenge. In the recent years, wind energy is gaining more importance among the researchers worldwide. Wind energy is intermittent in nature and hence the power system scheduling and dynamic control of wind turbine requires an estimate of wind energy. Accurate forecast of wind speed is a challenging task for the wind energy research field. In fact, due to the large variability of wind speed caused by the unpredictable and dynamic nature of the earth's atmosphere, there are many fluctuations in wind power production. This inherent variability of wind speed is the main cause of the uncertainty observed in wind power generation. Furthermore, producing wind power forecasts might be obtained indirectly by modeling the wind speed series and then transforming the forecasts through a power curve. Wind speed forecasting techniques have received substantial attention recently and several models have been developed. Basically two main approaches have been proposed in the literature: (1) physical models such as Numerical Weather Forecast and (2) statistical models such as Autoregressive integrated moving average (ARIMA) models, Neural Networks. While the initial focus in the literature has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. In short term context, probabilistic forecasts might be more relevant than point forecasts for the planner to build scenarios In this paper, we are interested in estimating predictive intervals of the hourly wind speed measures in few cities in United Arab emirates (UAE). More precisely, given a wind speed time series, our target is to forecast the wind speed at any specific hour during the day and provide in addition an interval with the coverage probability 0

Transition from Research to Operations: Assessing Value of Experimental Forecast Products within the NWSFO Environment

NASA Technical Reports Server (NTRS)

Lapenta, William M.; Wohlman, Richard; Bradshaw, Tom; Burks, Jason; Jedlovec, Gary; Goodman, Steve; Darden, Chris; Meyer, Paul

2003-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center seeks to accelerate the infusion of NASA Earth Science Enterprise (ESE) observations, data assimilation and modeling research into NWS forecast operations and decision-making. To meet long-term program expectations, it is not sufficient simply to give forecasters sophisticated workstations or new forecast products without fully assessing the ways in which they will be utilized. Close communication must be established between the research and operational communities so that developers have a complete understanding of user needs. In turn, forecasters must obtain a more comprehensive knowledge of the modeling and sensing tools available to them. A major goal of the SPoRT Program is to develop metrics and conduct assessment studies with NWS forecasters to evaluate the impacts and benefits of ESE experimental products on forecast skill. At a glance the task seems relatively straightforward. However, performing assessment of experimental products in an operational environment is demanding. Given the tremendous time constraints placed on NWS forecasters, it is imperative that forecaster input be obtained in a concise unobtrusive manor. Great care must also be taken to ensure that forecasters understand their participation will eventually benefit them and WFO operations in general. Two requirements of the assessment plan developed under the SPoRT activity are that it 1) Can be implemented within the WFO environment; and 2) Provide tangible results for BOTH the research and operational communities. Supplemental numerical quantitative precipitation forecasts (QPF) were chosen as the first experimental SPoRT product to be evaluated during a Pilot Assessment Program conducted 1 May 2003 within the Huntsville AL National Weather Service Forecast Office. Forecast time periods were broken up into six- hour bins ranging from zero to twenty-four hours. Data were made available for display in AWIPS on an operational basis so they could be efficiently incorporated into the forecast process. The methodology used to assess the value of experimental QPFs compared to available operational products is best described as a three-tier approach involving both forecasters and research scientists. Tier-one is a web-based survey completed by duty forecasters on the aviation and public desks. The survey compiles information on how the experimental product was used in the forecast decision making process. Up to 6 responses per twenty-four hours can be compiled during a precipitation event. Tier-two consists of an event post mortem and experimental product assessment performed daily by the NASA/NWS Liaison. Tier-three is a detailed breakdown/analysis of specific events targeted by either the NWS SO0 or SPoRT team members. The task is performed by both NWS and NASA research scientists and may be conducted once every couple of months. The findings from the Pilot Assessment Program will be reported at the meeting.

Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

PubMed

Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

2015-04-21

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

PubMed Central

Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

2015-01-01

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698

A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

NASA Astrophysics Data System (ADS)

Sheffield, Justin; He, Xiaogang; Wood, Eric; Pan, Ming; Wanders, Niko; Zhan, Wang; Peng, Liqing

2017-04-01

Sustainable management of water resources and mitigation of the impacts of hydrological hazards are becoming ever more important at large scales because of inter-basin, inter-country and inter-continental connections in water dependent sectors. These include water resources management, food production, and energy production, whose needs must be weighed against the water needs of ecosystems and preservation of water resources for future generations. The strains on these connections are likely to increase with climate change and increasing demand from burgeoning populations and rapid development, with potential for conflict over water. At the same time, network connections may provide opportunities to alleviate pressures on water availability through more efficient use of resources such as trade in water dependent goods. A key constraint on understanding, monitoring and identifying solutions to increasing competition for water resources and hazard risk is the availability of hydrological data for monitoring and forecasting water resources and hazards. We present a global online system that provides continuous and consistent water products across time scales, from the historic instrumental period, to real-time monitoring, short-term and seasonal forecasts, and climate change projections. The system is intended to provide data and tools for analysis of historic hydrological variability and trends, water resources assessment, monitoring of evolving hazards and forecasts for early warning, and climate change scale projections of changes in water availability and extreme events. The system is particular useful for scientists and stakeholders interested in regions with less available in-situ data, and where forecasts have the potential to help decision making. The system is built on a database of high-resolution climate data from 1950 to present that merges available observational records with bias-corrected reanalysis and satellite data, which then drives a coupled land surface model-flood inundation model to produce hydrological variables and indices at daily, 0.25-degree resolution, globally. The system is updated in near real-time (< 2 days) using satellite precipitation and weather model data, and produces forecasts at short-term (out to 7 days) based on the Global Forecast System (GFS) and seasonal (up to 6 months) based on U.S. National Multi-Model Ensemble (NMME) seasonal forecasts. Climate change projections are based on bias-corrected and downscaled CMIP5 climate data that is used to force the hydrological model. Example products from the system include real-time and forecast drought indices for precipitation, soil moisture, and streamflow, and flood magnitude and extent indices. The model outputs are complemented by satellite based products and indices based on satellite data for vegetation health (MODIS NDVI) and soil moisture (SMAP). We show examples of the validation of the system at regional scales, including how local information can significantly improve predictions, and examples of how the system can be used to understand large-scale water resource issues, and in real-world contexts for early warning, decision making and planning.

Long-term US energy outlook

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

Friesen, G.

Chase Econometrics summarizes the assumptions underlying long-term US energy forecasts. To illustrate the uncertainty involved in forecasting for the period to the year 2000, they compare Chase Econometrics forecasts with some recent projections prepared by the DOE Office of Policy, Planning and Analysis for the annual National Energy Policy Plan supplement. Scenario B, the mid-range reference case, is emphasized. The purpose of providing Scenario B as well as Scenarios A and C as alternate cases is to show the sensitivity of oil price projections to small swings in energy demand. 4 tables.

Averaging business cycles vs. myopia: Do we need a long term vision when developing IRP?

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

McDonald, C.; Gupta, P.C.

1995-05-01

Utility demand forecasting is inherently imprecise due to the number of uncertainties resulting from business cycles, policy making, technology breakthroughs, national and international political upheavals and the limitations of the forecasting tools. This implies that revisions based primarily on recent experience could lead to unstable forecasts. Moreover, new planning tools are required that provide an explicit consideration of uncertainty and lead to flexible and robust planning tools are required that provide an explicit consideration of uncertainty and lead to flexible and robust planning decisions.

Water resources adaptation to climate and demand change in the Potomac river

USDA-ARS?s Scientific Manuscript database

The effects of climate change are increasingly considered in conjunction with changes in water demand and reservoir sedimentation in forecasts of water supply vulnerability. Here, the relative effects of these factors are evaluated for the Washington, DC metropolitan area water supply for the near f...

Agri-Manpower Forecasting and Educational Planning

ERIC Educational Resources Information Center

Ramarao, D.; Agrawal, Rashmi; Rao, B. V. L. N.; Nanda, S. K.; Joshi, Girish P.

2014-01-01

Purpose: Developing countries need to plan growth or expansion of education so as to provide required trained manpower for different occupational sectors. The paper assesses supply and demand of professional manpower in Indian agriculture and the demands are translated in to educational requirements. Methodology: The supply is assessed from the…

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...

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.