A Wind Power Prediction Method Based on DE-BP Neural Network

Abstract

With the continuous increase of installed capacity of wind power, the influence of large-scale wind power integration on the power grid is becoming increasingly apparent. Ultra-short-term wind power prediction is conducive to the dispatching management of the power grid and improves the operating efficiency and economy of the power system. In order to overcome the intermittency and uncertainty of wind power generation, this article proposes the differential evolution–back propagation (DE-BP) algorithm to predict wind power and addresses such shortcomings of the BP neural network as its falling into local optimality and slow training speed when predicting. In this article, the DE algorithm is used to find the optimal value of the initial weight and threshold of the BP neural network, and the DE-BP neural network prediction model is obtained. According to the data of a wind farm in Northwest China, the short-term wind power is predicted. Compared with the application of the BP model in wind power prediction, the results show that the accuracy of the DE-BP algorithm is improved by about 5%; compared with the genetic algorithm–BP model, the prediction time is shortened by 23.1%.