Wind Speed Forecasting Using LSSVM Model Based On a Novel Optimization Algorithm

Abstract

Wind speed forecasting has significant influence on wind energy development. In the paper, a least square support vector machine with a novel optimization algorithm was used to improve the performances of wind speed forecasting model. Coupled simulated annealing (CSA) and simplex algorithm were combined in the novel optimization algorithm to optimize parameters of LSSVM forecasting model. Firstly, parameters were optimized by CSA in global scope. Then, parameters which got from CSA were optimized by simplex algorithm to get the best parameters. Finally, the LSSVM model with best parameters was applied to wind speed forecasting. Based on the data obtained from a wind farm in Shanxi province, the simulation results show that comparing with the support vector machine (SVM) model with grid-search and the LSSVM model with particle swarm optimization, the proposed model has better performances on accuracy and training time, thereby it helps make reasonable decisions for power scheduling and dispatch. Keywords-wind speed forecasting; least squares support vector machine (LSSVM); coupled simulated annealing algorithm; simplex algorithm