A novel ensemble model of different mother wavelets for wind speed multi-step forecasting

Accurate wind speed forecasting is essential for smart wind power conversion and integration. In the study, a novel ensemble model, using four novel hybrid models as base predictors to obtain high prediction accuracy, is proposed for the multi-step wind speed forecasting. The hybrid base predictors consist of the Wavelet Packet Decomposition (WPD), the Multi-Objective Grey Wolf Optimizer (MOGWO), the Adaptive Boosting.MRT (AdaBoost.MRT) and the Outlier-Robust Extreme Learning Machine (ORELM). The proposed ensemble model is named as the MOGWO-WPD -AdaBoost.MRT-ORELM model. The accuracy and diversity of the base predictors have significant positive influences on the performance of the proposed ensemble model. To guarantee the diversity of the base predictors, one of the most important hyper-parameters in the WPD computation (i.e., the mother wavelet) for every base predictor is investigated. In addition, the MOGWO is used to assemble the base predictors. By combining various models with different hyper-parameters, the ensemble structure can be used to improve the forecasting performance of the hybrid model with single hyper-parameter. To investigate the performance of the proposed forecasting architecture, four sets of experiments were conducted in the study. The results show that: (a) the proposed ensemble model has good convergence and forecasting performance; (b) the forecasting accuracy of the base predictor increases as the vanishing moment increases; and (c) the proposed ensemble model outperforms other benchmark models significantly.