摘要
介绍了一种基于粗集理论与神经网络相结合的电力系统负荷预测方法.运用粗集理论方法对不确定、不完整的历史数据进行属性的约简分析,并将约简后的属性作为人工神经网络的输入进行负荷预测;改进基因算法对神经网络权值修正.算例表明该方法可行、有效.
Based on the rough set theory forecast and artificial neural network, a new method of load forecasting is put forward. The rough set is used to analyze the condition attributes reduction based on uncertain and incomplete original data. These attributes are then applied to the artificial neural network as the input vectors to forecast load. The improved genetic algorithm is used to amend neural network weights. The calculation examples show that the presented method is feasible and effective.
出处
《江南大学学报（自然科学版）》
CAS
2008年第4期459-462,共4页
Joural of Jiangnan University (Natural Science Edition)
关键词
粗集理论
人工神经网络
负荷预测
基因算法
rough set theory
artificial neural network
load forecasting
genetic algorithm
