A vanishing moment ensemble model for wind speed multi-step prediction with multi-objective base model selection

Abstract

Wind speed forecasting model can improve the safety and economy of wind energy utilization. In this study, a new hybrid ensemble model is presented. In the proposed ensemble model, the base models are the wavelet-based models with 10 different vanishing moments. The base models are selected by multi-objective feasibility enhanced particle swarm optimization algorithm to maximize relevancy and minimize redundancy. In this manner, the redundant base models can be discarded, while enhancing the relevancy between the forecasting results of the base models and actual data. Then, the ensemble weights of the selected base models are calculated by non-dominated sorting genetic algorithm III. The optimization objective function is optimizing the accuracy and robustness of the ensemble model. Three real wind speed series collected from three different sites in Xinjiang are utilized for data simulation. The experimental results show that: (1) the proposed hybrid model can generate high-accuracy wind speed forecasting results in the studied cases; (2) the proposed hybrid model can outperform other benchmark models in the studied cases.