Jianqi An^1,2,3, Zhangbing Chen^1,2, Min Wu^1,2, Takao Terano³, Min Ding^1,2 and Hua Xie^1,2, ¹China University of Geosciences, China, ²Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China and ³Tokyo Institute of Technology, Japan

Wind power prediction (WPP) is of great importance to the safety of the power grid and the effectiveness of power generations dispatching. However, the accuracy of WPP obtained by single numerical weather prediction (NWP) is difficult to satisfy the demands of the power system. In this research, we proposed a WPP method based on Bayesian fusion and multisource NWPs. First, the statistic characteristics of the forecasted wind speed of each-source NWP was analysed, pre-processed and transformed. Then, a fusion method based on Bayesian method was designed to forecast the wind speed by using the multi-source NWPs, which is more accurate than any original forecasted wind speed of each-source NWP. Finally, the neural network method was employed to predict the wind power with the wind speed forecasted by Bayesian method. The experimental results demonstrate that the accuracy of the forecasted wind speed and wind power prediction is improved significantly.

Wind Power Prediction, Numerical Weather Prediction, Bayesian Fusion, Wind Speed Prediction