Unified whale optimization algorithm based multi-kernel SVR ensemble learning for wind speed forecasting

Support vector regression (SVR) is widely used in the field of wind speed forecasting because of its excellent nonlinear learning ability. However, the drawback of SVR is the model selection problem, which has the high complexity O(K×m3) including kernel function selection and parameter selection. To solve this problem, this paper proposes a multi-kernel SVR ensemble (MKSVRE) model based on unified optimization and whale optimization algorithm (WOA), where the MKSVRE model is used to solve the kernel function selection problem, and the unified optimization and the WOA are used to solve the parameter selection problem. The proposed model provides an alternative without the need to specifically select a kernel function and thus enhances the adaptability of SVR to diverse data. In addition, the unified optimization takes into account the interactions between models and achieves a global parameter selection. The proposed model is tested by simulations on wind speed data from Shandong Province, China. By comparing the prediction results of the proposed model, the single kernel SVR models, the models before and after optimization, and six other models, the effectiveness of the proposed model is confirmed.