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。
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.
出处
《电工技术学报》
EI
CSCD
北大核心
2005年第1期95-98,共4页
Transactions of China Electrotechnical Society
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
Character of statistic, short time load forecast, cascaded neural network, RBPNN
