We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{\"i}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are outperformed by intelligent learning algorithms. In particular, the CART-Bagging algorithm gives very stable and promising results. Besides, as a step towards forecast, we quantify the impact of using deteriorated wind measures on the performances. We show also on this application that the default methodology to select a subset of predictors provided in the standard random forest package can be refined, especially when there exists among the predictors one variable which has a major impact.
@article{arxiv.1610.01000,
title = {Statistical learning for wind power : a modeling and stability study towards forecasting},
author = {Aurélie Fischer and Lucie Montuelle and Mathilde Mougeot and Dominique Picard},
journal= {arXiv preprint arXiv:1610.01000},
year = {2019}
}