Wind speed prediction using support vector regression

In this paper the wind speed forecasting in a wind farm, applying the algorithm of support vector regression (SVR) to the mean 10-minute time series is presented. By comparing its performance with an back propagation neural network model through simulation results, we could find following facts: firstly, both algorithms are applicable for prediction the wind speed time series in future; secondly, the prediction effect of support vector regression outperforms the back propagation neural network model as indicated by the prediction graph and by the mean square errors and mean absolute errors. Finally, we selected three different stages of the wind speed curve to analyze, the results show that the proposed algorithm fit the original wind speed curve well at the whole process, but the back propagation neural network is inapplicability for the rise stage when the ascent rate suddenly become flatness of the original wind speed curve.