Precise Wind Power Prediction with SVM Ensemble Regression

In this work, we propose the use of support vector regression ensembles for wind power prediction. Ensemble methods often yield better classification and regression accuracy than classical machine learning algorithms and reduce the computational cost. In the field of wind power generation, the integration into the smart grid is only possible with a precise forecast computed in a reasonable time. Our support vector regression ensemble approach uses bootstrap aggregating (bagging), which can easily be parallelized. A set of weak predictors is trained and then combined to an ensemble by aggregating the predictions. We investigate how to choose and train the individual predictors and how to weight them best in the prediction ensemble. In a comprehensive experimental analysis, we show that our SVR ensemble approach renders significantly better forecast results than state-of-the-art predictors.