NDLI: Genetic algorithm-piecewise support vector machine model for short term wind power prediction

Content Provider	IEEE Xplore Digital Library
Author	Jie Shi Yongping Yang Peng Wang Yongqian Liu Shuang Han
Copyright Year	2010
Description	Author affiliation: Renewable Energy School, North China Electric Power University, No.2 BeiNong Road, Beijing, 102206, China (Yongqian Liu; Shuang Han) \|\| Thermal Energy and Power Engineering School, North China Electric Power University, No.2 BeiNong Road, Beijing, 102206, China (Jie Shi; Yongping Yang; Peng Wang)
Abstract	Short term wind power prediction is one of the effective ways to cope with the operational problems caused by the variability of the wind energy resource when a large penetration of wind power is integrated into electric power systems. In this paper, a model combining a Genetic Algorithm with a Piecewise Support Vector Machine (GA-PSVM) is developed that improves the precision of short term wind power prediction systems based on power curves of the wind turbine generator systems. A Genetic Algorithm (GA) is used to search automatically for the parameters of a Piecewise Support Vector Machine (PSVM) model. The resulting GA-PSVM model can be used to predict wind power generation from one to six hours ahead. Operational data from a wind farm in North China are used to evaluate the proposed model. The results show that the mean relative errors (MRE) of GA-PSVM model are 2.03% lower than that of the standard SVM model applied to the same data set.
Starting Page	2254
Ending Page	2258
File Size	853772
Page Count	5
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554305
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Wind power generation Predictive models Wind turbines Wind speed Data models Wind farms genetic arithmetic wind power prediction support vector machine
Content Type	Text
Resource Type	Article

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