Forecasting Short-Term Wind Speed Based on IEWT-LSSVM Model Optimized by Bird Swarm Algorithm

Abstract

Accurate wind speed prediction can improve the utilization efficiency of wind energy effectively. However, the original time series signals of wind speed present nonlinear or non-stationary characteristics, which make wind speed forecasting very difficult. A new pre-processing method of wind speed signal, named improved empirical wavelet transform (IEWT), is proposed in this paper, which is inspired by the spectrum segmentation theory of EWT. Then, a novel hybrid model combining IEWT and least square support vector machine (LSSVM) is put forward to predict short-term wind speed. In order to achieve better prediction results, the parameters are jointly optimized through bird swarm algorithm (BSA) in this model. The prediction performance of the established hybrid model is tested with practical wind speed signal of four seasons from a wind farm situated in Zhejiang, China. The experimental results indicate that the presented hybrid forecasting model can effectively follow the change of wind speed, which exhibits more superior predicting performance than other popular models.