Wind power prediction by cascaded clustering method and wavelet neural network

Accurate wind power prediction can improve the economic operation and safety management of power system. The accuracy of wind power forecast could be improved if the training samples have the similar variation with the predicting day. A cascaded hybrid clustering method which contained both Euclidean distance and cosine angle distance is proposed to extract the most similar training samples. A wavelet neural network based on improved particle swarm optimization (IPSO) is adopted to optimize the wind power output. The predicting strategy is applied to wind power forecast in a wind farm of west china, the results show that the similar samples are identified and the predicting accuracy is improved effectively. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.