基于数据挖掘的工业用户用电行为分析

徐磊; 杨秀; 张美霞

徐磊,杨秀,张美霞.基于数据挖掘的工业用户用电行为分析[J].电测与仪表,2017,54(16):.
XU Lei,YANG Xiu,ZHANG Meixia.Industrial Users of Electricity Behavior Analysis Based on Data Mining[J].Electrical Measurement & Instrumentation,2017,54(16):.

基于数据挖掘的工业用户用电行为分析

Industrial Users of Electricity Behavior Analysis Based on Data Mining

DOI：

中文关键词: 工业用户 K-means聚类算法初始聚类数初始聚类中心用电模式提取用电行为分析

英文关键词:Industrial users K-means algorithm initial cluster numbers initial cluster centers electricity pattern extraction electricity behavior analysis

基金项目:国家电网公司科技项目资助(520940150010; 52094015001L)

作者中文名	作者英文名	单位
徐磊	XU Lei	上海电力学院电气工程学院
杨秀	YANG Xiu	上海电力学院电气工程学院
张美霞	ZHANG Meixia	上海电力学院电气工程学院

摘要点击次数: 2460

中文摘要:

本文以上海市部分地区工业用户为研究对象,利用数据挖掘技术分析其用电行为。根据用户档案采集和整合用电数据,同时对数据进行修复和归一化预处理；综合考虑聚类数的确定及初始聚类中心的选择这两个因素,对K-means算法进行优化；利用优化的算法对用户负荷曲线分类并提取特征曲线,分析其用电行为典型特征,并与传统的K-means算法进行比较,同时引入相关指标检验聚类效果。结果表明,采用优化的K-means聚类算法能准确实现不同用户类型的分类识别功能,可以更加准确有效的进行用户用电行为的分析。

英文摘要:

In this paper, the Shanghai industrial users in some areas is studied using data mining techniques to analyze its behavior of electricity. According to user profile data acquisition and integration of electricity data, the data is repaired and normalized. Considering two factors that the number of clusters and selection of the initial cluster centers to improve the K-means algorithm, the improved K-means algorithm is used in data classification to extract all types of users clustering characteristic curve,then analyze the typical characteristics of behavior of electricity, and compared with the traditional K-means algorithm and relevant indicators is introduced to test clustering effect. The results show that improved K-means clustering algorithm can realize the different types of user classification function and can be more accurately and effectively analyze the behavior of users of electricity .

查看全文查看/发表评论下载PDF阅读器

关闭