Short-term wind power prediction with combination of speed and power time series

Abstract

The integration of wind power generation into the power grid can only succeed with precise and reliable forecast methods. With different measurements available, machine learning algorithms can yield very good predictions for short-term forecast horizons. In this paper, we compare the use of wind power and wind speed time series as well as differences of subsequent measurements with Random Forests, Support Vector Regression and k-nearest neighbors. While both time series, wind power and speed, are well-suited to train a predictor, the best performance can be achieved by using both together. Further, we propose an ensemble approach combining RF and SVR with a cross-validated weighted average and show that the prediction performance can be substantially improved.