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Forecasting day-ahead price of electricity - a dynamic regression approach

Author

Listed:
  • Amitava Bandyopadhayay
  • Soumya Roy
  • Dipojjwal Ghosh

Abstract

The electricity market is being deregulated all over the world. Deregulation has brought in a variety of trading systems such as day-ahead trading and has also introduced high volatility of electricity prices. The large variability of price increases the risk for the market participants and forces the business houses to look for a forecasting accuracy of about ± 3%. This paper provides a method to predict next-day electricity prices using dynamic regression methodology where the price was regressed on selected demand, as well as supply side variables available in the public domain, and the error has been modelled using ARIMA/SARIMA models. The results were found to be very encouraging with MAPE lying in the range of ± 3.5% in most cases. In order to reduce the complexity associated with developing many models, a clustering methodology was used to group the different hours of the day so as to reduce the number of forecasting models to be fitted. Agglomerative hierarchical clustering with single linkage was used and models for representative hours had the required level of accuracy.

Suggested Citation

  • Amitava Bandyopadhayay & Soumya Roy & Dipojjwal Ghosh, 2013. "Forecasting day-ahead price of electricity - a dynamic regression approach," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 6(5), pages 584-604.
    • Handle: RePEc:ids:ijbexc:v:6:y:2013:i:5:p:584-604
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    Cited by:

    1. Abeer Alshejari & Vassilis S. Kodogiannis & Stavros Leonidis, 2020. "Development of Neurofuzzy Architectures for Electricity Price Forecasting," Energies, MDPI, vol. 13(5), pages 1-24, March.

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