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A comparison of univariate methods for forecasting electricity demand up to a day ahead

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  • Taylor, James W.
  • de Menezes, Lilian M.
  • McSharry, Patrick E.

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  • 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.
  • Handle: RePEc:eee:intfor:v:22:y:2006:i:1:p:1-16
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    References listed on IDEAS

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    1. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    2. 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.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    4. Souza, Leonardo Rocha & Soares, Lacir Jorge, 2003. "Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil," FGV/EPGE Economics Working Papers (Ensaios Economicos da EPGE) 491, FGV/EPGE - Escola Brasileira de Economia e Finan├žas, Getulio Vargas Foundation (Brazil).
    5. Taylor, James W. & Buizza, Roberto, 2003. "Using weather ensemble predictions in electricity demand forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 57-70.
    6. 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.
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