The research and application of wavelet-support vector machine on short-term wind power prediction

Author

Shi, Jie ; Liu, Yongqian ; Yang, Yongping ; Han, Shuang ; Wang, Peng

Author_Institution

Thermal Energy & Power Eng. Sch., North China Electr. Power Univ., Beijing, China

fYear

2010

Firstpage

4927

Lastpage

4931

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. For the time frame of one hour ahead, the average error of optimal wind turbine prediction method is 12.07%.

Keywords

power engineering computing; radial basis function networks; support vector machines; wavelet transforms; wind power plants; wind turbines; North China; SVM; mean relative error; short-term wind power prediction; wavelet-support vector machine; wind power prediction; wind turbine generator systems; Data models; Mathematical model; Predictive models; Support vector machines; Wavelet transforms; Wind power generation; Wind speed; support vector machine; wavelet transformation; wavelet-support vector machine model; wind power prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2010 8th World Congress on

Conference_Location

Jinan

Print_ISBN

978-1-4244-6712-9

Type

conf

DOI

10.1109/WCICA.2010.5554823

Filename

5554823