Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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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)
  • Received : 2016.06.29
  • Accepted : 2017.01.17
  • Published : 2017.05.01
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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.

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