Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil
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References listed on IDEAS
- Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006.
"Forecasting electricity demand using generalized long memory,"
International Journal of Forecasting,
Elsevier, pages 17-28.
- Soares, Lacir Jorge & Souza, Leonardo Rocha, 2003. "Forecasting electricity demand using generalized long memory," FGV/EPGE Economics Working Papers (Ensaios Economicos da EPGE) 486, FGV/EPGE - Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
- Arteche, Josu & Robinson, Peter M., 1998. "Seasonal and cyclical long memory," LSE Research Online Documents on Economics 2241, London School of Economics and Political Science, LSE Library.
- Ferrara, Laurent & Guegan, Dominique, 2001.
"Forecasting with k-Factor Gegenbauer Processes: Theory and Applications,"
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 20(8), pages 581-601, December.
- Laurent Ferrara & Dominique Guegan, 2001. "Forecasting with k-factor Gegenbauer Processes: Theory and Applications," Post-Print halshs-00193667, HAL.
- 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.
- repec:crs:wpaper:9927 is not listed on IDEAS
- Laurent Ferrara & Dominique Guegan, 1999. "Estimation and Applications of Gegenbauer Processes," Working Papers 99-27, Center for Research in Economics and Statistics.
- Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
- Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
- 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.
- Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
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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.
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NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ALL-2004-06-02 (All new papers)
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