[1]徐敏,袁建洲,刘四新,等.基于改进粒子群优化算法的短期风电功率预测[J].郑州大学学报(工学版),2012,33(06):32-35.[doi:10.3969/j.issn.1671-6833.2012.06.008]
　XU Min,YUAN Jianzhou,LIU Sixin.Short-term Wind Power Prediction Based on ModifiedParticle Swarm Optimization Algorithm[J].Journal of Zhengzhou University (Engineering Science),2012,33(06):32-35.[doi:10.3969/j.issn.1671-6833.2012.06.008]
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基于改进粒子群优化算法的短期风电功率预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
33
期数:
2012年06期
页码:
32-35
栏目:
出版日期:
2012-11-10

文章信息/Info

Title:
Short-term Wind Power Prediction Based on ModifiedParticle Swarm Optimization Algorithm
作者:
徐敏; 袁建洲; 刘四新; 等.
南昌大学信息工程学院,江西南昌,330031, 江西省安福县供电公司,江西安福,343200, 河南省禹州市电力工业公司,河南禹州,461670
Author(s):
XU Min; YUAN Jianzhou; LIU Sixin
1. Information Engineering School, Nanchang University, Nanchang 330031 ,China; 2. Jiangxi An’fu Power Supply Company,An’fu 343200,China; 3.Henan Power Supply Company, Yuzhou 461670,China
关键词:
支持向量机;  风电功率预测;  改进粒子群优化算法;  精度
Keywords:
SVM;  wind power prediction ; MPSO ; precision
分类号:
TM614
DOI:
10.3969/j.issn.1671-6833.2012.06.008
摘要:
针对传统支持向量机(SVM)模型在风电功率预测中存在的参数选取问题,提出一种新的预测模型,采用改进的粒子群(MPSO)优化算法寻求SVM的最优参数模型,经典粒子群算法是一种全局优化算法,在此基础上提出改进的粒子群算法.算例结果表明,经MPSO优化的SVM模型应用于短期风电功率预测是有效的,使其预测精度有所提高.
Abstract:
In view of the parameter selection problems existing in the traditional support vector machine( SVM ) model in wind power prediction,this paper puts forward a new forecasting model: with modified parti.cle swarm optimization algorithm ( MPSO ) for the optimal parameters of the SVM model, the classical PSO isa global optimization algorithm. Based on it, the modified PSO( MPSO )is proposed. Results show that theSVM model optimized by the MPSO is effective in short-term wind power prediction, and the prediction preci.sion is improved.

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更新日期/Last Update: 1900-01-01