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Electricity price forecasting through transfer function models

Author

Listed:
  • F J Nogales

    (Universidad Carlos III de Madrid)

  • A J Conejo

    (Universidad de Castilla-La Mancha)

Abstract

Forecasting electricity prices in presentday competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes a transfer function model to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build it. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The proposed model is compared with naïve and other techniques.

Suggested Citation

  • F J Nogales & A J Conejo, 2006. "Electricity price forecasting through transfer function models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 350-356, April.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:4:d:10.1057_palgrave.jors.2601995
    DOI: 10.1057/palgrave.jors.2601995
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    References listed on IDEAS

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    1. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    2. M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, vol. 12(4), pages 287-298, October.
    3. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
    4. Antonio Conejo & Francisco Prieto, 2001. "Mathematical programming and electricity markets," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 9(1), pages 1-22, June.
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