Search

• A novel hybrid model is proposed to predict wind power time series. • Multi-resolution multi-learner ensemble scheme is designed. • A metaheuristic optimization-based adaptive model selection method is developed. • Proposed model has advantages in robustness, adaptiveness and accuracy. 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. [ABSTRACT FROM AUTHOR]

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is then used to obtain the optimal BESS size. Finally, the paper presents quantitative analyses of the impact of both the energy predictors and the BESS on the supplied energy cost using the actual data of the Summerside Electric grid. The paper reveals the significant potential for reducing energy cost in renewable-penetrated grids and microgrids through state-of-the-art predictors combined with applications of properly-sized energy storage systems. [ABSTRACT FROM AUTHOR]