Forecasting of wind speed with least squares support vector machine based on genetic algorithm

Accurate short-term wind speed forecasting is very important to improve the security and stability of power grid and to reduce the running cost. In this paper, a method based on Least squares support vector machine (LS-SVM) was proposed to the short-term forecasting. In order to avoid the blindness and inaccuracy of Parameter selection, Genetic algorithm is used to select the optimal regularization parameter C and kernel parameter δ of the LS-SVM. Through the actual numerical example, the method effectively improved the prediction of the reliability and accuracy.