基于小波包分析的电力负荷预测算法  

Power Load Forecasting Algorithm Based on Wavelet Packet Analysis

作　　者：张大海[1] 江世芳[1] 

机构地区：[1]山东大学电气工程学院,济南250061

出　　处：《电力系统及其自动化学报》2004年第2期51-53,84,共4页Proceedings of the CSU-EPSA

摘　　要：提出基于小波包分解和重构的电力负荷预测算法。算法使用具有线性相位的双正交小波对电力负荷数据进行小波包分解和重构 ,然后用神经网络直接对各尺度上的电力负荷分量进行预测 ,最后将各尺度上的预测值相加 ,得到实际负荷预测值。算例表明算法具有较高的预测精度 ,优于传统的 BP神经网络 ,有利于分析不同时频区域的电力负荷特性 。This paper investigated the application of wavelet packet ananlysis in power system load forecasting,and proposed a power load forecasting algorithm based on wavelet packet decomposition and reconstruction.The algorithm used the biorthogonal wavelet which has linear phase to decompose and reconstruct the power load data,then used neural network to predict the load series in each scale.Finally,the load forecasting values of each scale are summed up to produce the load forcasting result.Case study showed that the new algorithm improves forecasting accuracy and is superiror to traditional back-propagation neural network.Furthermore,it is useful for analyzing the load characteristics in each time-frequency zone,thus it provides necessary information for more accurate modelling and forecasting.

关 键 词：电力负荷预测算法 小波包分析 人工神经网络 小波理论 电力系统 

分 类 号：TM715[电气工程—电力系统及自动化]