A Multi-Model Combination Approach for Probabilistic Wind Power Forecasting

Abstract

Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. It would be difficult to develop a universal forecasting model dominating over other alternative models because of the inherent stochastic nature of wind power. Therefore, a novel multi-model combination (MMC) approach for probabilistic wind power forecasting is proposed in this paper to exploit the advantages of different forecasting models. The proposed approach can combine different forecasting models those provide different kinds of probability density functions to improve the performance of probabilistic forecasting. Three probabilistic forecasting models based on the sparse Bayesian learning, kernel density estimation, and beta distribution fitting are used to form the combined model. The parameters of the MMC model are solved by two-step optimization. Comprehensive numerical studies illustrate the effectiveness of the proposed MMC approach.