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基于级联神经网络的短期负荷概率预测新方法
引用本文:卫志农,王丹,孙国强,郑玉平. 基于级联神经网络的短期负荷概率预测新方法[J]. 电工技术学报, 2005, 20(1): 95-98
作者姓名:卫志农  王丹  孙国强  郑玉平
作者单位:河海大学电气工程学院,南京,210098;国电公司南京自动化研究院,南京,210003
摘    要:针对负荷历史数据的统计特征,提出一种基于RBPN网络和RBF网络的级联神经网络预测方法.本模型将历史负荷与其相对应的影响因素进行模式分类,由最大后验概率判别准则确定待预测日影响因素的模式,并利用其对应模式样本数据进行负荷预测.该算法减少了训练样本的数量,提高了预测精度,最后给出的算例证明该方法是合理有效的.

关 键 词:统计特征  短期负荷预测  级联网络  径向概率神经网络
修稿时间:2003-10-24

A Novel Method of Short Time Load Probability Forecasting Based on Cascaded Neural Network
Wei Zhinong,Wang Dan,Sun Guoqiang,Zheng Yuping. A Novel Method of Short Time Load Probability Forecasting Based on Cascaded Neural Network[J]. Transactions of China Electrotechnical Society, 2005, 20(1): 95-98
Authors:Wei Zhinong  Wang Dan  Sun Guoqiang  Zheng Yuping
Affiliation:Hohai University Nanjing 210098 China Nanjing Automation Research Institute Nanjing 210003 China
Abstract:To use the statistical character of past data on load , a Cascade Neural Network load forecasting method is proposed in this paper. In this modeling the influences of factors and past load on load forecasting are sorted. The method reduces the quantity of training set and improves the precision of forecasting , which makes use of the rule of maximum posteriori probability to confirm the pattern of forecasting day's factors and load. The experimental results show that the presented model is efficient and feasible.
Keywords:Character of statistic   short time load forecast   cascaded neural network   RBPNN
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