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Short-term wind power prediction based on wavelet transform-support vector machine and statistic characteristics analysis

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4 Author(s)
Jie Shi ; North China Electr. Power Univ., Beijing, China ; Yongqian Liu ; Yongping Yang ; Wei-Jen Lee

The prediction algorithm is an important key factor in wind power prediction. However, there are pros and cons on different forecasting algorithms. Based on the principles of wavelet transform (WT), support vector machine (SVM) as well as characteristics of wind turbine generation systems, two prediction methods are presented and compared in this paper. In method 1, the time series of model input are decomposed into different frequency composes and models are set up separately based on SVM. The results are combined together to obtain the final wind power output. In method 2, the wavelet kernel function is applied in place of RBF kernel function in SVM training. To supply more valuable suggestions, the means of evaluating prediction algorithm precision is proposed. The operation data from two wind farms both in North China and U.S.A are used to test the usability of the method. The mean relative error of WT-SVM model (method 1) is less than that of traditional SVM model.

Published in:

Industrial and Commercial Power Systems Technical Conference (I&CPS), 2011 IEEE

Date of Conference:

1-5 May 2011

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