Short-term wind power forecasting based on T-S fuzzy model

Due to the impacts of wind speed, wind direction, temperature and pressure, it is uncertain and nonlinear for the wind power forecasting. To address these problems, this paper proposes a wind power short-time forecasting method based on the T-S fuzzy model, which does not rely on a large amount of historical data and can linearize the complex nonlinear process to obtain accurate results. In this method, the main affecting factors are selected by means of the correlation analysis for wind power prediction. Then, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means (FCM) clustering algorithm and the recursive least squares method (RLS). Finally, the T-S fuzzy model for wind power short-term forecasting is obtained. The stationary wind periods are considered as the cases to validate the proposed forecasting method. The forecasting results are compared with the (support vector machine) SVM and the (empirical mode decomposition) EMD-SVM methods. The results show that the proposed T-S fuzzy model can effectively improve the precision of the short-term wind power forecasting.