一种基于小波神经元网络的短期负荷预测方法  

SHORT-TERM LOAD FORECASTING BASED ON WAVELET NEURAL NETWORK

作　　者：张步涵[1] 赵剑剑[1] 刘小华[1] 刘沛[1] 程时杰[1] 陆俭[2] 

机构地区：[1]华中科技大学电气与电子工程学院,湖北省武汉市430074 [2]武汉供电局,湖北省武汉市430074

出　　处：《电网技术》2004年第7期15-18,共4页Power System Technology

基　　金：高等学校博士学科专项科研基金资助项目(2000048712)

摘　　要：小波神经元网络比多层前馈神经网络具有更多自由度和更好的适应性。为更好地反映气象因素对负荷的影响及提高负荷预测的精度,文章选用Morlet小波构建小波神经元网络,采用误差反传学习算法来训练网络,采用自学习隶属度分析聚类的新方法选择训练样本。并应用武汉电网近年的负荷数据和气象资料进行了建模和预测,预测结果表明所建立的小波神经元网络预测模型具有较好的收敛性,采用自学习隶属度分析聚类方法选择训练样本能改善预测精度。Wavelet neural network (WNN) possesses more degree of freedom and better adaptivity than multi-layer FP neural network. To better reflect the influence of climate factors on load and improve the precision of load forecasting, the Morlet wavelet is chosen to establish a wavelet neuron network, the back propagate algorithm is adopted to train the WNN network, a new method of analyzing clustering by self-study membership is used to train the samples. The load data and climatic data of Wuhan power network in recent years are applied in modeling and load forecasting. The forecasting results show that the established WNN model possesses better convergence and the forecasting precision can be improved by choosing training samples with analyzing clustering by self-study membership.

关 键 词：电力系统 电网 短期负荷预测 小波 神经元网络 

分 类 号：TM715[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]