Statistical learning for wind power : a modeling and stability study towards forecasting

Aurélie Fischer (1) , Lucie Montuelle (1) , Mathilde Mougeot (1) , Dominique Picard (1)

1 UPD7 - Université Paris Diderot - Paris 7

We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Maïa 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.

Mots clés: bagging; forecasting; random forests; data mining; wind power; modeling; Stability

Domaines: Applications [stat.AP]; Machine Learning [stat.ML]; Ingénierie de l'environnement

Aurélie Fischer, Lucie Montuelle, Mathilde Mougeot, Dominique Picard. Statistical learning for wind power : a modeling and stability study towards forecasting. Wind Energy, 2017, 20 (12), pp.2037 - 2047. ⟨10.1002/we.2139⟩. ⟨hal-01373429v2⟩