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Forecasting daily demand for electricity with multiple-input nonlinear transfer function models: a case study

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  • Cancelo, José Ramón
  • Espasa, Antoni

Abstract

A model for forecasting the daily demand for electricity energy in Spain is presented. The trend and the weekly seasonality are modelled by using past values of the series; a complex intervention analysis is carried out to capture the effects of changes in the working conditions; and meteorological variables enter in the model with transfer functions, which allow nonlinear, dynamic, season-and type-ofday- dependent responses, as well as exhaustion effects and an implicit assessment of the increase on the stock of appliances.

Suggested Citation

  • Cancelo, José Ramón & Espasa, Antoni, 1991. "Forecasting daily demand for electricity with multiple-input nonlinear transfer function models: a case study," UC3M Working papers. Economics 2808, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:2808
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    Citations

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    Cited by:

    1. Espasa, Antoni & Cancelo, José Ramón & Revuelta, J. Manuel, 1996. "Automatic modelling of daily series of economic activity," DES - Working Papers. Statistics and Econometrics. WS 3356, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Espasa, Antoni & Carlomagno Real, Guillermo, 2023. "Tall big data time series of high frequency: stylized facts and econometric modelling," DES - Working Papers. Statistics and Econometrics. WS 37746, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. 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.
    4. Espasa, Antoni, 1993. "Modelling daily series of economic activity," DES - Working Papers. Statistics and Econometrics. WS 3682, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Eduardo Caro & Jesús Juan, 2020. "Short-Term Load Forecasting for Spanish Insular Electric Systems," Energies, MDPI, vol. 13(14), pages 1-26, July.

    More about this item

    Keywords

    Nonlinear ARMAX Models;

    Statistics

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