基于复Morlet小波SVM的负荷预测

基金项目:

江南娱乐网页版入口官网下载安装博士生创新基金的资助(2007-3)

详细信息

作者简介:
陈维荣(1965- ),男,教授,博士,从事综合自动化、智能检测技术,E-mail:wrchen@home.swjtu.edu.cn

通讯作者:
郑永康(1977- ),男,博士研究生,研究方向为负荷预测、计算智能等,E-mail:zyk555@163.com

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出版历程
- 收稿日期:2008-11-09
- 刊出日期:2009-11-12

Short-Term Load Forecasting Based on Complex Morlet Wavelet SVM

摘要

摘要:为提高预测精度和克服支持向量机(SVM)凭经验选择参数的不足,针对小波擅长信号细微特征提取和云遗传算法(CGA)良好的全局寻优能力,构建了以复Morlet小波为核函数、以CGA为参数优化算法的SVM——基于CGA的复Morlet小波SVM(CGA-CMW-SVM).针对短期负荷预测,为降低系统复杂性,克服负荷数据信息不完备、不精确的问题,仅仅利用了负荷的历史数据而不考虑气象和节假日等因素,在分析负荷时间序列混沌特性的基础上,对负荷数据进行相空间重构,并以相空间矢量作为CGA-CMW-SVM的输入,提出了短期负荷预测的新方法.仿真结果表明,该方法平均误差和最大误差小,平均误差在1.3400%以内,最小误差为1.0087%.
- 短期负荷预测/
- 相空间重构/
- 复Morlet小波核/
- 支持向量机/
- 云遗传算法
Abstract:In view of the advantage of wavelet analyses in subtle feature extraction and the good global optimization ability of cloud theory-based genetic algorithm (CGA),a CGA-based complex Morlet wavelet SVM (support vector machine),called CGA-CMW-SVM for short,was proposed to improve the forecasting precision and easily select the parameters of SVM.In the CGA-CMW-SVM,the complex Morlet wavelet is used as the kernel function,and the CGA is adopted to optimize the parameters.To decrease the system complexity in short-term load forecasting,the load time series were reconstructed based on the phase space reconstruction theory and their chaotic characteristics only by considering the historical load data without other factors,such as weather and holidays.Though it is believed that the single load data is often characterized as incomplete and inaccurate information,the phase space reconstruction can overcome the shortcomings.Then,the phase space vectors were used as the inputs of the CGA-CMW-SVM for short-term load forecasting.The simulation experiments show that the presented method has small average and maximum errors,and its average errors are less than 1.3400% with a minimum value of 1.0087%.
- short-term load forecasting/
- phase space reconstruction/
- complex Morlet wavelet kernel/
- support vector machine/
- cloud theory-based genetic algorithm

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参考文献 (1)

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