Hierarchical Bayesian modelling of the electricity load

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

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

In this paper, we study a non-linear model used to estimate and forecast the electricity load, that usually requires four or more years worth of data to avoid any overfitting phenomenon. We first propose a non-informative prior to be used when the number of observations is large enough. When the observations are too few, we propose a hierarchical prior to include information coming from another bigger, similar, sample. The posterior densities associated with these two priors are derived and a MCMC algorithm is provided in each case. We finally run these algorithms on simulated and real datasets ; the hierarchical prior greatly improves the quality of the model predictions.

Mots clés: informative prior; mcmc algorithms; small dataset; electricity load forecasting

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

Tristan Launay, Anne Philippe, Sophie Lamarche. Hierarchical Bayesian modelling of the electricity load. 2011. ⟨hal-00625117v1⟩