Medium-term wind power forecasting based on multi-resolution multi-learner ensemble and adaptive model selection

Abstract

Wind energy is a potential and prospective renewable energy resource. Wind power forecasting can enhance the stability and security of power integration. This study focuses on the univariate time series prediction of wind power. A hybrid wind power forecasting model based on multi-resolution multi-learner ensemble (MRMLE) and adaptive model selection (AMS), namely the MRMLE-AMS model is proposed. The proposed model adopts heterogeneous base learners and datasets with different resolutions to guarantee diversity. According to the multi-resolution ensemble scheme, the rapid fluctuation of original high-resolution data can be effectively considered, and a long prediction time scale is also achievable. A total of 16 sub-models are developed to generate preliminary forecasting results. To make the forecasting model adaptive and fit for different datasets, butterfly optimization algorithm (BOA) is employed to select these sub-models, and minimal-redundancy-maximal-relevance (mRMR) criterion is set as optimization objective. The selected sub-models are combined by support vector regression (SVR) to obtain final forecasting result. The proposed model is thoroughly analyzed and evaluated by four actual wind power series collected from Canada. Case study shows that the proposed MRMLE-AMS model has a rational framework and significantly outperforms the existing models. Finally, its advantages and limitations are given in the conclusion.