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Estimation et selection pour les modeles additifset application a la prevision de la consommation electrique

Vincent Thouvenot 1, 2
2 SELECT - Model selection in statistical learning
LMO - Laboratoire de Mathématiques d'Orsay, Inria Saclay - Ile de France
Abstract : French electricity load forecasting encounters major changes since the past decade. These changes are, among others things, due to the opening of electricity market (and economical crisis), which asks development of new automatic time adaptive prediction methods. The advent of innovating technologies also needs the development of some automatic methods, because we have to study thousands or tens of thousands time series. We adopt for time prediction a semi-parametric approach based on additive models. We present an automatic procedure for covariate selection in a additive model. We combine Group LASSO, which is selection consistent, with P-Splines, which are estimation consistent. Our estimation and model selection results are valid without assuming that the norm of each of the true non-zero components is bounded away from zero and need only that the norms of non-zero components converge to zero at a certain rate. Real applications on local and agregate load forecasting are provided. This phd has been achived in Orsay Mathematic Laboratory, EDF R&D and select Inria
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Contributor : Vincent Thouvenot <>
Submitted on : Tuesday, February 13, 2018 - 6:30:43 PM
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  • HAL Id : tel-01708576, version 1


Vincent Thouvenot. Estimation et selection pour les modeles additifset application a la prevision de la consommation electrique. Statistiques [stat]. Paris Saclay, 2015. Français. ⟨tel-01708576⟩



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