基于小波变换与支持向量机的短期电力负荷预测  

Short-Term Load Forecasting Based on Wavelet Transform and Support Vector Machine

作　　者：朱志勇[1] 林睦纲[2] 张深基[1] 

机构地区：[1]湖南工程学院,湘潭411101 [2]长沙理工大学计算机与通信工程学院,长沙410076

出　　处：《微计算机应用》2005年第4期440-442,共3页Microcomputer Applications

摘　　要：利用多分辨分析的小波变换对短期电力负荷序列进行分解处理,将负荷序列投影到不同的尺度上,对各子负荷序列根据其特性采用不同的支持向量机进行训练和预测,最后把各预测的结果叠加得到完整的负荷预测结果。算例结果表明该方法同支持向量机的方法相比较具有较高的预测精度和较强的适应能力。In this paper, the wavelet transform(WT) based on Multi-resolution Analysis(MRA) is applied to decompose short-term power load into wavelet component. By the WT, the different load sequence components are projects to the different scales. According the different character of the load components, the different support vector ma-chine(SVM) is used for forecasting. The forecast results is obtained by adding up all the forecast components. Simulation results demonstrate that the proposed method can offer higher forecast precision.

关 键 词：支持向量机 小波变换 电力负荷预测 短期电力负荷 多分辨分析 分解处理 预测结果 适应能力 预测精度 序列 投影 

分 类 号：TM715[电气工程—电力系统及自动化]