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短期电力负荷预测的小波支持向量机方法研究
引用本文:李元诚,方廷健,郑国祥.短期电力负荷预测的小波支持向量机方法研究[J].中国科学技术大学学报,2003,33(6):726-732.
作者姓名:李元诚  方廷健  郑国祥
作者单位:1. 中国科学技术大学自动化系,合肥,230026
2. 中国科学院智能机械研究所,合肥,230031
3. 空军雷达学院,武汉,430000
摘    要:在充分研究和比较多种负荷预测方法的基础上,提出一种称为小波支持向量机(Wavelet Support Vector Machines,WSVM)的负荷预测新算法.该方法是在研究支持向量机(SVM)核方法与小波框架理论的基础上,引入非线性小波基函数来构造SVM的核函数,从而得到新的SVM模型,并给出了此模型的结构设计与实现算法.通过实例验证,该方法能有效提高预测精度.

关 键 词:小波支持向量机  电力系统  短期负荷预测
文章编号:0253-2778(2003)06-0726-07

Wavelet Support Vector Machines for Short-term Load Forecasting
LI Yuan cheng ,FANG Ting jian ,ZHENG Guo xiang.Wavelet Support Vector Machines for Short-term Load Forecasting[J].Journal of University of Science and Technology of China,2003,33(6):726-732.
Authors:LI Yuan cheng  FANG Ting jian  ZHENG Guo xiang
Institution:LI Yuan cheng 1,FANG Ting jian 2,ZHENG Guo xiang 3
Abstract:On the basis of sufficient study and comparison of a variety methods of short term load forecasting, a new methodology called Wavelet Support Vector Machines (WSVM) is presented. The proposed algorithm is based on the Kernel method of Support Vector Machines (SVM) and wavelet frame, a wavelet basis was introduced to construct the kernel function of SVM. Furthermore, an algorithm for constructing and training the WSVM is presented. Analysis of the experimental results indicates that WSVM can achieve greater accuracy than the traditional methods.
Keywords:wavelet support vector machines  power system  short  term load forecasting
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