DocumentCode
2298110
Title
Short-term wind speed prediction based on LS-SVM
Author
Xiaojuan Han ; Fang Chen ; Hui Cao ; Xiangjun Li ; Xilin Zhang
Author_Institution
Coll. of Control & Comput. Eng., North China Electr. Power Univ., Beijing, China
fYear
2012
fDate
6-8 July 2012
Firstpage
3200
Lastpage
3204
Abstract
Accurate short-term wind speed prediction is very important to improve the security and stability of power grid. The method of least squares support vector machine (LS-SVM) for short-term wind speed prediction is proposed in this paper. In order to avoid inaccuracy of parameter selection and improve the accuracy of prediction, genetic algorithm is used to optimize the parameters of LS-SVM. It is proved that the method put forward in this paper can quickly and effectively realize short-term wind speed prediction by simulation example.
Keywords
least squares approximations; power engineering computing; power grids; support vector machines; wind; LS-SVM; genetic algorithm; least squares support vector machine; parameter selection; power grid; security; short-term wind speed prediction; stability; Forecasting; Genetic algorithms; Kernel; Power systems; Predictive models; Support vector machines; Wind speed; LS-SVM; genetic algorithm; parameters optimal; wind speed prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location
Beijing
Print_ISBN
978-1-4673-1397-1
Type
conf
DOI
10.1109/WCICA.2012.6358424
Filename
6358424
Link To Document