Title :
Wind power prediction with LSSVM model based on data stratification pretreatment
Author :
Zhang Wei ; Deng Yuan-chang ; Wei Zhen
Author_Institution :
Sch. of Eng., Sun Yat-Sen Univ., Guangzhou, China
Abstract :
Wind speed and wind power prediction are the keys to solve the wind power with grid problems. The invalid sample data affects the wind power model. To get the relationships of wind speed and wind power, layered statistics method is used to modify the wind power curve. This paper uses least square support vector machine model to predict the modified data. In order to verify the predicted effect, experienced power curve method is used for comparison. The results show that layered statistics method can eliminate the invalid data effectively and improve the accuracy of the prediction.
Keywords :
least squares approximations; power grids; statistical analysis; support vector machines; wind power; LSSVM model; data stratification pretreatment; grid problems; layered statistics method; least square support vector machine model; wind power curve method; wind power model; wind power prediction; wind speed prediction; Data models; Mathematical model; Power systems; Predictive models; Support vector machines; Wind power generation; Wind speed; LSSVM model; layered statistics; wind power prediction; wind speed prediction;
Conference_Titel :
Materials for Renewable Energy and Environment (ICMREE), 2013 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-3335-8
DOI :
10.1109/ICMREE.2013.6893683