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Modelling and forecasting short-term electricity load: a two step methodology

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  • Lacir J. Soares

    (Department of Electrical Engineering)

  • Marcelo Cunha Medeiros

    ()
    (Department of Economics PUC-Rio)

Abstract

The goal of this paper is to develop a forecasting model of the hourly electricity load demand in the area covered by an utility company located in the southeast of Brazil. A di®erent model is constructed for each hour of day, thus there are 24 di®erent models. Each model is based on a decomposition of the daily series of each hour in two components. The ¯rst component is purely deterministic and is related to trends, seasonality, and special days e®ect. The second one is stochastic and follows a linear autoregressive model. The multi-step forecasting performance of the proposed methodology is compared with a benchmark model and the results indicate that our proposal is a useful tool for electricity load forecasting.

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Bibliographic Info

Paper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 495.

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Length: 21p.
Date of creation: Feb 2005
Date of revision:
Handle: RePEc:rio:texdis:495

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  1. 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, Elsevier, vol. 16(1), pages 71-83.
  2. Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002. "Building neural network models for time series: A statistical approach," Working Paper Series in Economics and Finance 508, Stockholm School of Economics.
  3. Cottet R. & Smith M., 2003. "Bayesian Modeling and Forecasting of Intraday Electricity Load," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 98, pages 839-849, January.
  4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 31(3), pages 307-327, April.
  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, Elsevier, vol. 13(2), pages 161-174, June.
  6. Joanna Nowicka-Zagrajek & Rafal Weron, 2002. "Modeling electricity loads in California: ARMA models with hyperbolic noise," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/02/02, Hugo Steinhaus Center, Wroclaw University of Technology.
  7. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, Econometric Society, vol. 48(4), pages 817-38, May.
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Cited by:
  1. Jaume Rosselló Nadal & Mohcine Bakhat, 2009. "A new approach to estimating tourism-induced electricity consumption," CRE Working Papers (Documents de treball del CRE), Centre de Recerca Econòmica (UIB ·"Sa Nostra") 2009/6, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
  2. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, Elsevier, vol. 22(1), pages 17-28.

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