A hybrid forecasting approach applied to wind speed time series

In this paper, a hybrid forecasting approach, which combines the Ensemble Empirical Mode Decomposition (EEMD) and the Support Vector Machine (SVM), is proposed to improve the quality of wind speed forecasting. The essence of the methodology incorporates three phases. First, the original data of wind speed are decomposed into a number of independent Intrinsic Mode Functions (IMFs) and one residual series by EEMD using the principle of decomposition. In order to forecast these IMFs, excepting the highest frequency acquired by EEMD, the respective estimates are yielded using the SVM algorithm. Finally, these respective estimates are combined into the final wind speed forecasts using the principle of ensemble. The proposed hybrid method is examined by forecasting the mean monthly wind speed of three wind farms located in northwest China. The obtained results confirm an observable improvement for the forecasting validity of the proposed hybrid approach. This tool shows great promise for the forecasting of intricate time series which are intrinsically highly volatile and irregular. •A hybrid approach is put forward to solve the wind speed with high volatility and irregularity.•The proposed hybrid method can integrate the advantages of other individual models.•The hybrid method contributes to boosting the model forecasting capacity and enhancing forecasting efficiency.•Empirical results demonstrate that the proposed method is a promising tool to forecast complex time series.