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http://dx.doi.org/10.5370/JEET.2017.12.3.989

Wind Power Interval Prediction Based on Improved PSO and BP Neural Network  

Wang, Jidong (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University)
Fang, Kaijie (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University)
Pang, Wenjie (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University)
Sun, Jiawen (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University)
Publication Information
Journal of Electrical Engineering and Technology / v.12, no.3, 2017 , pp. 989-995 More about this Journal
Abstract
As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.
Keywords
Wind power prediction; Interval prediction; BP neural network; Improved PSO;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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