Evolutionary feature weighting for wind power prediction with nearest neighbor regression

Optimizing the weighting of features significantly improves the predictions in regression tasks. In this paper, we employ evolution strategies to evolve distance measures in a spatio-temporal regression approach for short-term wind prediction. The well-understood nearest neighbor regression method is the basis of our study. We compare a classic feature selection approach based on binary representations to the evolvement of continuous feature weights with the CMA-ES. The latter scales the original feature space and turns out to be the most successful approach in an experimental analysis on five benchmark turbines. We compare to standard nearest neighbor regression and concentrate on the interplay of training, validation, and test sets with a focus on overfitting the prediction model.