Journal
WIND ENERGY
Volume 20, Issue 12, Pages 2037-2047Publisher
WILEY
DOI: 10.1002/we.2139
Keywords
bagging; data mining; forecasting; modeling; random forests; stability; wind power
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Funding
- French National Research Agency (ANR) [ANR-14-CE05-0028]
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We focus onwind power modeling using machine learning techniques. We show on real data provided by the wind energy companyMaa 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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