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An hourly periodic state space model for modelling French national electricity load

  • Dordonnat, V.
  • Koopman, S.J.
  • Ooms, M.
  • Dessertaine, A.
  • Collet, J.

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for each hour of the day. Dependence between the equations is introduced by covariances between disturbances that drive the time-varying processes. The equations are estimated simultaneously. Our model consists of components that represent trends, seasons at different levels (yearly, weekly, daily, special days and holidays), short-term dynamics and weather regression effects, including nonlinear functions for heating effects. The implementation of our forecasting procedure relies on the multivariate linear Gaussian state space framework, and is applied to the national French hourly electricity load. The analysis focuses on two hours, 9 AM and 12 PM, but forecasting results are presented for all twenty-four hours. Given the time series length of nine years of hourly observations, many features of our model can be estimated readily, including yearly patterns and their time-varying nature. The empirical analysis involves an out-of-sample forecasting assessment up to seven days ahead. The one-day ahead forecasts from forty-eight bivariate models are compared with twenty-four univariate models, one for each hour of the day. We find that the implied forecasting function depends strongly on the hour of the day.

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Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 24 (2008)
Issue (Month): 4 ()
Pages: 566-587

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Handle: RePEc:eee:intfor:v:24:y:2008:i:4:p:566-587
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  1. 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.
  2. Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998. "Statistical Algorithms for Models in State Space Using SsfPack 2.2," Discussion Paper 1998-141, Tilburg University, Center for Economic Research.
  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. 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.
  5. Pedregal, Diego J. & Young, Peter C., 2006. "Modulated cycles, an approach to modelling periodic components from rapidly sampled data," International Journal of Forecasting, Elsevier, vol. 22(1), pages 181-194.
  6. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
  7. 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.
  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. repec:cep:stiecm:/1992/241 is not listed on IDEAS
  10. Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2005. "Modelling the hourly Spanish electricity demand," Economic Modelling, Elsevier, vol. 22(3), pages 551-569, May.
  11. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543.
  12. Smith M. & Kohn R., 2002. "Parsimonious Covariance Matrix Estimation for Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1141-1153, December.
  13. Rong Chen & John L. Harris & Jun M. Liu & Lon-Mu Liu, 2006. "A semi-parametric time series approach in modeling hourly electricity loads," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(8), pages 537-559.
  14. 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.
  15. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
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