| 基于支持向量机的短期负荷预测的方法改进 |
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| 引用本文： | 李云飞,黄彦全,蒋功连.基于支持向量机的短期负荷预测的方法改进[J].西华大学学报(自然科学版),2007,26(2):31-34. |
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| 作者姓名： | 李云飞 黄彦全 蒋功连 |
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| 作者单位： | 西南交通大学电气工程学院,四川,成都,610031 |
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| 摘 要： | 在对支持向量机（Support Vector Machines,SVM）方法的参数性能进行分析的基础上,提出了将Grid-search方法引入至基于支持向量机的短期负荷预测算法中,以解决支持向量机方法的参数选择问题。该参数选择方法减少了参数选择的盲目性,提高了SVM的预测精度。通过在East-Slovakia Power Distribution Company提供的电网运行数据上验算,证明了该改进方法的正确性和有效性。
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| 关 键 词： | 短期负荷预测 支持向量机 电力系统 核函数 参数选择 |
| 文章编号： | 1673-159X（2007）02-0031-04 |
| 修稿时间： | 2006-10-122007-01-16 |
An Improved Method for Short-Term Load Forecasting Based on Support Vector Machines |
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| LI Yun-fei,HUANG Yan-quan,JIANG Gong-lian.An Improved Method for Short-Term Load Forecasting Based on Support Vector Machines[J].Journal of Xihua University:Natural Science Edition,2007,26(2):31-34. |
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| Authors: | LI Yun-fei HUANG Yan-quan JIANG Gong-lian |
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| Abstract: | Grid-search method,which aims to solve the parameters selection problems for SVM method,is introduced to short-term load forecasting based on support vector machines after the parameter performance of support vector machines(SVM) is analyzed.This method reduces the blindness for the choice of an appropriate set of parameters,and it is helpful to improve the prediction precision of SVM method.Experiments are carried out on the basis of the history data offered by East-Slovakia Power Distribution Company.Simulation results show that this improved algorithm is valid and effective. |
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| Keywords: | short-term load forecasting support vector machine power system kernels parameter selection |
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