Wind Speed Forecasting Based on Chaotic Particle Swarm Optimization Support Vector Machine

Abstract

It is difficult to merge wind power into grid, owing to wind power’s uncertainty and prediction inaccuracy. Wind speed is an important factor affecting wind power, so the accuracy of wind speed prediction has a major impact on the wind power prediction. The support vector machine (SVM) has been successfully applied in the short-term wind speed forecasting field, but its learning and generalization ability depend on proper setting of its parameters. In order to improve forecasting accuracy, aiming at the disadvantages like man-made blindness in the parameters selection of SVM, in this paper, the chaos theory was applied to the particles swarm optimization (PSO) algorithm in order to cope with the problems such as low search speed and local optima. Finally, we used it to optimize the support vector machines of short-term wind speed forecasting model. Through the analysis of the forecasting results, it is shown that the proposed method could reduce modeling error and forecasting error of SVM model effectively and has better performance than general methods.