On particle filters applied to electricity load forecasting

Tristan Launay (1, 2) , Anne Philippe (1) , Sophie Lamarche (2)

1 LMJL - Laboratoire de Mathématiques Jean Leray
2 EDF R&D - EDF R&D

We are interested in the online prediction of the electricity load, within the Bayesian framework of dynamic models. We offer a review of sequential Monte Carlo methods, and provide the calculations needed for the derivation of so-called particles filters. We also discuss the practical issues arising from their use, and some of the variants proposed in the literature to deal with them, giving detailed algorithms whenever possible for an easy implementation. We propose an additional step to help make basic particle filters more robust with regard to outlying observations. Finally we use such a particle filter to estimate a state-space model that includes exogenous variables in order to forecast the electricity load for the customers of the French electricity company Électricité de France and discuss the various results obtained.

Mots clés: dynamic model; particle filter; sequential Monte Carlo; electricity load forecasting

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Tristan Launay, Anne Philippe, Sophie Lamarche. On particle filters applied to electricity load forecasting. 2013. ⟨hal-00737555v2⟩