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短期负荷预测中支持向量机模型的参数选取和优化方法
引用本文:谢宏,魏江平,刘鹤立. 短期负荷预测中支持向量机模型的参数选取和优化方法[J]. 中国电机工程学报, 2006, 26(22): 17-22
作者姓名:谢宏  魏江平  刘鹤立
作者单位:1. 上海海事大学信息工程学院,上海市,浦东区,200135
2. 江苏信息职业技术学院,江苏省,无锡市214061
基金项目:上海市重点学科建设项目(T0602)。
摘    要:讨论了基于支持向量机的电力系统负荷预测模型建模方法。对建立支持向量机模型,通过对模型结构的分析,提出了模型学习参数的选取方法,给出了基于最优方向搜索的高斯核函数参数的优化算法。针对负荷预测模型,对模型的输入变量采用规范化预处理消除了量纲不一致对SVM模型的不利影响,通过对数变换将模型输出的相对误差转化为模型输出的绝对误差,便于模型学习参数的选择。最后采用实际数据对该方法进行了模拟计算,其结果表明该方法可以有效地降低SVM模型的建模误差和测试误差,而且SVM模型比模糊模型和神经网络模型有更好的泛化性能和预测精度。

关 键 词:负荷预测  支持向量机  高斯核函数  参数选取  泛化性能
文章编号:0258-8013(2006)22-0017-06
收稿时间:2006-05-21
修稿时间:2006-05-21

Parameter Selection and Optimization Method of SVM Model for Short-term Load Forecasting
XIE Hong,WEI Jiang-ping,LIU He-li. Parameter Selection and Optimization Method of SVM Model for Short-term Load Forecasting[J]. Proceedings of the CSEE, 2006, 26(22): 17-22
Authors:XIE Hong  WEI Jiang-ping  LIU He-li
Affiliation:1. Faculty of Information Engineering, Shanghai Maritime University, Pudong District, Shanghai 200135, China ; 2. Jiangsu College of Information Technology, Wuxi 214061, Jiangsu Province, China
Abstract:The modeling method of electrical load forecasting model, which is based on SVM, is mainly discussed in this paper. To build SVM model, a new approach of selecting learning parameters is proposed on analysis of model structure, and an optimizing algorithm of Gauss kernel parameter is presented based on local optimal searching direction. For load forecasting model, input variables is normalized to reduce the influence of different units for SVM model, and logarithm transform is applied to convert relative error of model output into absolute error, in such a way it is convenient to selecting learning parameters. A actual data is employed to simulate computing, the result shows that proposed methods could reduce modeling error and testing error of SVM model, which has better generalized performance and accuracy than fuzzy model and artificial neural network model.
Keywords:load forecasting  support vector machines  gauss kernel  parameter selection  generalization
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