On particle filters applied to electricity load forecasting

Launay, Tristan; Philippe, Anne; Lamarche, Sophie

Journal de la société française de statistique, Tome 154 (2013) no. 2, pp. 1-36.

Dans cet article nous proposons un modèle dynamique, non-linéaire, pour prévoir en ligne la consommation d’électricité. Nous présentons une revue des méthodes séquentielles de Monte Carlo utilisées pour le calcul des filtres particulaires. Nous discutons les principaux problèmes qui surviennent lors de l’utilisation de ces filtres et nous décrivons les algorithmes associés aux solutions. Nous introduisons une nouvelle étape qui permet la détection et la suppression automatique des données aberrantes qui conduisent souvent à la dégénerescence de la distribution des particules. Nous appliquons ensuite un algorithme de filtrage particulaire afin d’estimer notre modèle de consommation et comparons les prévisions obtenues aux prévisions opérationnelles utilisées au sein d’EDF.

In this paper, 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.

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