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

arXiv: https://arxiv.org/abs/1610.01000 · HTML: https://mlsanity.com/paper/1610.01000/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2016.

## Built on by (most cited first, 1 of 1)

- [Consensual Aggregation on Random Projected High-dimensional Features for Regression](https://mlsanity.com/paper/2204.02606/) (2022)

## Abstract (arXiv)

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