LAUSR

Abstract Accurate wind speed forecasting plays a crucial role in wind power plants and wind systems. Numerous studies that dedicated to enhance wind speed forecasting accuracy have been proposed. Nevertheless, some classical models are not good at dealing with nonlinear time sequence, and artificial intelligence are easy fall into local optimum. Based on this, this paper proposed a novel combined model for wind speed forecasting, which combined hybrid models based on decomposition method and optimization algorithm. In this approach, four distinct hybrid models are proposed and to additionally enhance the forecasting performance, ESN (Echo state network) is initially applied to integrate all the results obtained by each hybrid model and achieve the ultimate forecasting results. Besides, a distinct data set division mechanism is adopted in the multi-step wind speed forecasting. To verify the effectiveness and versatility of the proposed combined model, the wind speed data of 20 m, 50 m, 80 m per minute of the M2 tower from the National Wind Power Technology Center, United States, are utilized as case studies. The experimental results indicate that the proposed combined model performs better than other conventional methods.