Wind farm power prediction based on wavelet decomposition and chaotic time series |
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| Authors: | Xueli An Dongxiang Jiang Chao Liu Minghao Zhao |
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| Affiliation: | 1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;1. School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China;2. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;3. Department of Software Engineering, University of Technology, Sydney, Australia;1. Schulich School of Engineering, University of Calgary, Alberta, Canada;2. Electrical Engineering Department, Semnan University, Semnan, Iran |
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| Abstract: | In this paper, a prediction model is proposed for wind farm power forecasting by combining the wavelet transform, chaotic time series and GM(1, 1) method. The wavelet transform is used to decompose wind farm power into several detail parts associated with high frequencies and an approximate part associated with low frequencies. The characteristic of each high frequencies signal is identified, if it is chaotic time series then use weighted one-rank local-region method to predict it. If not, use GM(1, 1) model to predict it. And the GM(1, 1) model is also used to predict the approximate part of the low frequencies. In the end, the final forecasted result for wind farm power is obtained by summing the predicted results of all extracted high frequencies and the approximate part. According to the predicted results, the proposed method can improve the prediction accuracy of the wind farm power. |
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