Intelligent combined prediction of wind power based on numerical weather prediction and fuzzy clustering

Wind power prediction accuracy has important implications for the scheduling and stable operation of the power system. An intelligent combined prediction algorithm of wind power based on numerical weather prediction and fuzzy clustering was proposed in the paper. Based on numerical weather prediction (NWP) data and using the method of fuzzy clustering subtraction, the original NWP data is divided into several typical weather patterns; T-S fuzzy model, time-series model, multiple linear regression model and gray model are established respectively according to different wheather types; the combination of multi-model is optimized using intelligent optimization algorithms and the optimal combination prediction model is obtained. Prediction results of a domestic wind farm indicated that the proposed combination prediction method is valid and effective in short-term wind power prediction with better prediction accuracy. © 2017, Editorial Board of Acta Energiae Solaris Sinica. All right reserved.