Short-Term Prediction of Wind Farm Power Based on PSO-SVM

被引：0

作者：

Wang, He ^{[1
]}

Hu, Zhijian ^{[1
]}

Hu, Mengyue ^{[1
]}

Zhang, Ziyong ^{[1
]}

机构：

[1] Wuhan Univ, Sch Elect Engn, Wuhan 430072, Peoples R China

来源：

2012 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC) | 2012年

关键词：

wind farm power; prediction; particle swarm optimization (PSO); support vector machine (SVM); PSO-SVM;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In order to improve the precision of wind power prediction, an improved particle swarm optimization (PSO) is used to get the global optimal solution for the three parameters which affect the regression performance of Support Vector Machine (SVM). The SVM regression model with optimized parameters was used to predict the short-term (12 hours) wind power of a wind farm in North China. For comparative analysis, a traditional SVM prediction model is used as well. Compared with the traditional SVM, the forecast results show that the PSO-SVM method applied in this paper has effectively improved the prediction accuracy and reduced the forecast error.

引用

页数：4

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