Coupling framework for a wind speed forecasting model applied to wind energy

Wind energy is the burgeoning renewable energy. Accurate wind speed prediction is necessary to ensure the stability and reliability of the power grid for wind energy. This study focuses on developing a novel hybrid forecasting model to tackle adverse effects caused by strong variability and abrupt changes in wind speed. The hybrid model combines data decomposition and error correction strategy for a wind speed forecasting model applied to wind energy. First, wavelet packet decomposition is applied to wind speed series to obtain stationary subseries. Next, outlier robust extreme learning machine is implemented to predict subseries. Finally, an error correction strategy coupled with data decomposition is designed to repair preliminary prediction results. In addition, four measured datasets from China and USA wind farms with different time intervals are used to evaluate the performance of the proposed approach. Experimental analysis indicates that the proposed model outperforms the compared models. Results show that (1) the prediction accuracy of the proposed model is remarkably improved compared with other conventional models; (2) the proposed model can reduce the influence of the end effect in the decomposition-based forecasting model; (3) the coupling framework is successful for enhancing performance of hybrid forecasting model.