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《Proceedings of the CSEE》 2009-28
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Short-term Power Prediction of a Wind Farm Based on Wavelet Analysis

WANG Li-jie1, DONG Lei1, LIAO Xiao-zhong1, GAO Yang2 (1. School of Automation, Beijing Institute of Technology, Haidian District, Beijing 100081, China; 2. Department of Electrical Engineering, Shenyang Institute of Engineering, Shenyang 110136, Liaoning Province, China)  
This paper studied the short-term prediction of wind power generating capacity by means of wavelet analysis and artificial neural network. Signal was decomposed into several sequences in different band by wavelet tranform. By building different neural network, decomposed time series were predicted separately, then the predicted results were added. Some simulations were performed using the real data from Fujin wind farm, China. The results show that the neural network model based on wavelet decomposition improves time lag problem and the mean absolute error drops from 6.99% to 6.01%, compared with the neural network model based on chaotic characteristic.
【Fund】: 国家自然科学基金项目(50777003);; 中国电力投资公司科技项目(2009065ZDGKJX)~~
【CateGory Index】: TM614
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