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Forecasting from one day to one week ahead for the Spanish system operator

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  • Jose Ramon Cancelo

    ()

  • Antoni Espasa

    ()

  • Rosemarie Grafe

    ()

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    Abstract

    This paper discusses the building process and models used by Red Eléctrica de España (REE), the Spanish system operator, in short-term electricity load forecasting. REE's forecasting system consists of one daily model and 24 hourly models with a common structure. There are two types of forecasts of special interest to REE, several days ahead predictions for daily data and one day ahead hourly forecasts. Accordingly, forecast accuracy is assessed in terms of their errors. For doing so we analyze historical, real time forecasting errors for daily and hourly data for the year 2006, and report forecasting performance by day of the week, time of the year and type of day. Other aspects of the prediction problem, like the influence of the errors in predicting temperature on forecasting the load several days ahead, or the need for an adequate treatment of special days, are also investigated.

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    File URL: http://e-archivo.uc3m.es/bitstream/10016/1146/1/ws078418.pdf
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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws078418.

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    Date of creation: Dec 2007
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    Handle: RePEc:cte:wsrepe:ws078418

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    Related research

    Keywords: Energy forecasting; Hourly and daily models; Time series; Forecasting practice;

    This paper has been announced in the following NEP Reports:

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    1. Henley, Andrew & Peirson, John, 1997. "Non-linearities in Electricity Demand and Temperature: Parametric versus Non-parametric Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(1), pages 149-62, February.
    2. Schultz, Randall L., 1992. "Fundamental aspects of forecasting in organizations," International Journal of Forecasting, Elsevier, vol. 7(4), pages 409-411, March.
    3. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January.
    4. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    5. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    6. Smith, Michael, 2000. "Modeling and Short-term Forecasting of New South Wales Electricity System Load," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 465-78, October.
    7. Hippert, H.S. & Bunn, D.W. & Souza, R.C., 2005. "Large neural networks for electricity load forecasting: Are they overfitted?," International Journal of Forecasting, Elsevier, vol. 21(3), pages 425-434.
    8. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    9. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2003. "Forecasting Electricity Demand Using Generalized Long Memory," Economics Working Papers (Ensaios Economicos da EPGE) 486, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
    10. Cottet R. & Smith M., 2003. "Bayesian Modeling and Forecasting of Intraday Electricity Load," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 839-849, January.
    11. Jose Ramon Cancelo & Antoni Espasa, 1996. "Modelling and forecastng daily series of electricity demand," Investigaciones Economicas, Fundación SEPI, vol. 20(3), pages 359-376, September.
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