Electricity Forecasting Using Multi-Stage Estimators of Nonlinear Additive Models

French electricity load forecasting has encountered major changes during the past decade. These changes are, among other things, due to the opening of the electricity market and the economic crisis, which require the development of new automatic time adaptive prediction methods. The advent of innovating technologies also needs the development of some automatic methods because thousands or tens of thousands of time series have to be studied. In this paper we adopt for prediction a semi-parametric approach based on additive models. We present an automatic procedure for explanatory variable selection in an additive model and show how to correct middle term forecasting errors for short term forecasting. First, we consider an application to the EDF customer load demand which is typical of a load demand at an aggregate level. The goal of the application is to select variables from a large explanatory variables dictionary. The second application presented is an application on load demand of GEFCom 2012 competition, which we consider as a local application, where a major difficulty is to select some meteorological stations.