Abstract: In order to improve the forecast precision, an improved wind speed forecasting algorithm was discussed. The new method has modified extreme points and processed offset of predicting data, considering with the extreme points of the change in wind speed affecting the prediction error and the delay of prediction curve compared with actual wind speed. The forecasting model has better prediction accuracy and better computing speed to predict wind speed for the next one hour, compared with the wind speed model based on least squares support vector machine optimized by particle swarm optimization algorithm(PSO-LS-SVM), least squares support vector machine (LS-SVM) and back propagation (BP) neural network. The simulation results show that the improved least squares support vector machine is an effective method for short-term wind forecasting.

Key words:  wind speed forecasting, particle swarm optimization (PSO), least squares support vector machine (LSSVM), extreme points, offset