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

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

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.

Suggested Citation

  • Badri, Masood A. & Al-Mutawa, Ahmed & Davis, Donald & Davis, Donna, 1997. "EDSSF: A decision support system (DSS) for electricity peak-load forecasting," Energy, Elsevier, vol. 22(6), pages 579-589.
    • Handle: RePEc:eee:energy:v:22:y:1997:i:6:p:579-589
    DOI: 10.1016/S0360-5442(96)00163-6
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    Cited by:

    1. Marco Barassi & Yuqian Zhao, 2018. "Combination Forecasting of Energy Demand in the UK," The Energy Journal, , vol. 39(1_suppl), pages 209-238, June.
    2. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    3. Mirlatifi, A.M. & Egelioglu, F. & Atikol, U., 2015. "An econometric model for annual peak demand for small utilities," Energy, Elsevier, vol. 89(C), pages 35-44.

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