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arXiv · 1610.01000

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

Aurélie Fischer·Lucie Montuelle·Mathilde Mougeot·Dominique Picard
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Abstract

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.

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Aurélie Fischer, Lucie Montuelle, Mathilde Mougeot, Dominique Picard. 2016-10-04. Statistical learning for wind power : a modeling and stability study towards forecasting. https://doi.org/10.1002/we.2139

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