Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (1): 166-174.doi: 10.16088/j.issn.1001-6600.2021060912

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Research on Wind Power Short-term Prediction Based on EEMD-GA-BP Model

ZHU Enwen*, ZHU Anqi, WANG Jiedan, LIU Yujiao   

  1. School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha Hunan 410114, China
  • Received:2021-06-09 Revised:2021-08-05 Online:2022-01-25 Published:2022-01-24

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Abstract: With the rapid development of China’s wind power industry, the scale of wind power grid integration is constantly expanding. Accurate prediction of wind farm output power is an effective way to reduce the impact of wind power fluctuations on the power grid, which can improve power quality, and ensure the stable operation of the power grid. In this paper, the method of box analysis and hot card filling is used to clean and reconstruct the abnormal data in the data set. The BP algorithm is improved by combining genetic algorithm and EEMD decomposition algorithm. According to the comparison of prediction results with different time scales, the EEMD-GA-BP model has higher prediction accuracy and more stable prediction effect compared with the traditional prediction model.

Key words: wind power short-term prediction, back propagation neural network, integrated empirical mode decomposition, genetic algorithm

CLC Number: 

  • O212.1
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