Short­term Wind Power Prediction Based on Wavelet­Support Vector Machine and Statistic Characteristics Analysis

Yongqian Liu1, Jie Shi1, Yongping Yang1, Peng Wang2

Renewable Energy School, North China Electric Power University, Beijing, China

shi j i e0921@ gm ail . com

Thermal Energy and Power Engineering School, North China Electric Power University, Beijing, China

wan gpeng5 640112 6@ 126. com

Abstract

The arithmetic of wind power prediction plays an important part in the development of wind power prediction. In this paper, based on the principles of support vector machine (SVM) and wavelet, the wavelet SVM model for short term wind power prediction is built up along with analyzing the characteristics of power curves of wind turbine generator systems. The operation data from a wind farm in North China are used to test the proposed model, the mean relative error of wavelet SVM model is 6.05% less than that of traditional RBF SVM model. Based on the prediction results and statistic characteristics of prediction error, the result scope with certain confidence interval is got in order to process uncertainties analysis.

Keywords wind power prediction, wavelet transformation, support vector machine, wavelet­support vector machine model, uncertainty analysis

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