基于结构自适应神经网络用电量时间特征的聚类分析  

Clustering Analysis for Time Feature of User Power Consumption Based on Structural Self-adaptation ANN

作　　者：袁忠军[1] 陈刚[2] 

机构地区：[1]广西水利电力职业技术学院电力工程系,广西南宁530023 [2]重庆大学电气工程学院高电压与电工新技术教育部重点实验室,重庆400030

出　　处：《重庆大学学报（自然科学版）》2007年第8期44-48,共5页Journal of Chongqing University

基　　金：重庆市科委自然科学基金资助项目(CSTC;2006BB6209)

摘　　要：鉴于聚类分析在数据挖掘中具有重要的作用,针对聚类分析中聚类数确定难的问题,深入研究了聚类准则的选择和曲线特性,提出了一种基于SOFM神经网络的结构自适应聚类神经网络,其特点是能够自动确定最佳的聚类数。基于实际营销数据,采用结构自适应聚类神经网络技术实现了用户用电量时间特征分析,所得结论对于电价的针对性的调整以及合理地安排电力生产具有重要的参考价值。In view of the important effect of clustering analysis in data mining, the clustering rules and its curve are studied to solve the problem of determining clustering number. A kind of self-adaptation clustering ANN is presented based on SOFM ANN, which can automatically determine the clustering number. Based on practical sales data, the time feature analysis of power user consumption are carried out by using the self-adaptation clustering ANN, whose conclusion has the important referenced values for adjusting power price correspondingly and arranging power producing reasonably.

关 键 词：聚类分析 最优聚类数 人工神经网络 用电量时间特征 

分 类 号：TM715[电气工程—电力系统及自动化]