LAUSR

Abstract Wind speed forecasting plays a prominent part in the operation of wind power plants and power systems. However, it is often difficult to obtain satisfactory prediction results because wind speed data comprise random nonlinear series. Current some statistical models are not proficient in predicting nonlinear time series, whereas artificial intelligence models often fall into local optima. For these reasons, a novel combined forecasting model, which combines hybrid models based on decomposed methods and optimization algorithms, is successfully developed with variable weighting combination theory for multi-step wind speed forecasting. In this model, three different hybrid models are proposed and to further improve the forecasting performance, a modified support vector regression is used to integrate all the results obtained by each hybrid model and obtain the final forecasting results. To verify the forecasting effectiveness of the proposed forecasting model, 10-min wind speed series from Penglai, China, are used as case studies. The experimental results indicate that the developed combined model not only outperforms other benchmark models but also can be satisfactorily used for planning for smart grids.