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