EDSSF: A decision support system (DSS) for electricity peak-load forecasting

Masood A. Badri; Ahmed Al-Mutawa; Donald Davis; Donna Davis

doi:10.1016/S0360-5442(96)00163-6

EDSSF: A decision support system (DSS) for electricity peak-load forecasting

Masood A. Badri, Ahmed Al-Mutawa, Donald Davis, Donna Davis

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

Electricity authorities in the UAE have not been successful in developing reliable and accurate models of system peak load. In this study, we develop a time-series-based decision-support system that integrates data management, model base management, simulation, graphic display, and statistical analysis to provide near-optimal forecasting models. The model base includes a variety of time-series techniques, such as exponential smoothing, Box-Jenkins (BJ), and dynamic regression. The system produces short-term forecasts (one year ahead) by analyzing the behavior of monthly peak loads. The performance of the DSS is validated through a comparison with results suggested by econometricians.

Original language	English
Pages (from-to)	579-589
Number of pages	11
Journal	Energy
Volume	22
Issue number	6
DOIs	https://doi.org/10.1016/S0360-5442(96)00163-6
Publication status	Published - Jun 1997

ASJC Scopus subject areas

Civil and Structural Engineering
Building and Construction
Modelling and Simulation
Renewable Energy, Sustainability and the Environment
Fuel Technology
Energy Engineering and Power Technology
Pollution
Energy(all)
Mechanical Engineering
Industrial and Manufacturing Engineering
Management, Monitoring, Policy and Law
Electrical and Electronic Engineering

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title = "EDSSF: A decision support system (DSS) for electricity peak-load forecasting",

abstract = "Electricity authorities in the UAE have not been successful in developing reliable and accurate models of system peak load. In this study, we develop a time-series-based decision-support system that integrates data management, model base management, simulation, graphic display, and statistical analysis to provide near-optimal forecasting models. The model base includes a variety of time-series techniques, such as exponential smoothing, Box-Jenkins (BJ), and dynamic regression. The system produces short-term forecasts (one year ahead) by analyzing the behavior of monthly peak loads. The performance of the DSS is validated through a comparison with results suggested by econometricians.",

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AU - Badri, Masood A.

AU - Al-Mutawa, Ahmed

AU - Davis, Donald

AU - Davis, Donna

PY - 1997/6

Y1 - 1997/6

N2 - Electricity authorities in the UAE have not been successful in developing reliable and accurate models of system peak load. In this study, we develop a time-series-based decision-support system that integrates data management, model base management, simulation, graphic display, and statistical analysis to provide near-optimal forecasting models. The model base includes a variety of time-series techniques, such as exponential smoothing, Box-Jenkins (BJ), and dynamic regression. The system produces short-term forecasts (one year ahead) by analyzing the behavior of monthly peak loads. The performance of the DSS is validated through a comparison with results suggested by econometricians.

AB - Electricity authorities in the UAE have not been successful in developing reliable and accurate models of system peak load. In this study, we develop a time-series-based decision-support system that integrates data management, model base management, simulation, graphic display, and statistical analysis to provide near-optimal forecasting models. The model base includes a variety of time-series techniques, such as exponential smoothing, Box-Jenkins (BJ), and dynamic regression. The system produces short-term forecasts (one year ahead) by analyzing the behavior of monthly peak loads. The performance of the DSS is validated through a comparison with results suggested by econometricians.

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JF - Energy

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EDSSF: A decision support system (DSS) for electricity peak-load forecasting

Abstract

ASJC Scopus subject areas

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