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%.
Highlights
Wind energy is one of the renewable energy resources and the most available resource with the lowest power generation cost
Compared to the application of the back propagation (BP) model in wind power prediction, the results show that the accuracy of the differential evolution–back propagation (DE-BP) algorithm is improved by about 5%
This article proposes a hybrid method for wind power prediction, which is based on a feedforward neural network trained through a combination of the differential evolution (DE) and BP algorithms
Summary
Ultrashort-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. 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%
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