电力系统保护与控制Issue(19)：68-73,6.
基于聚类分析的客户用电模式智能识别方法
Application of clustering analysis in typical power consumption profile analysis
摘要
Abstract
In order to gain the large power customers’ typical power consumption profiles in a power supply area, a new clustering evaluation method is presented and a clustering analysis framework based on k-means, k-medoids, self-organized maps (SOM) and Fuzzy C-Means (FCM) is built. It analyzes the characteristic of the electricity consumption data and uses the Gaussian smoothing method to reduce the noise in the data. Clusters average radius, clusters average diameter and clusters average minimum distance are proposed and used to design the clustering evaluation method. This framework is utilized to analyze the daily electricity consumption curves of the whole customers in a certain area, which can automatically recognize the number of clusters. The result shows this methodology is clear in physical conception, simple and practical.关键词
用电模式分析/高斯核函数平滑/聚类效果评估/聚类分析Key words
power consumption profile analysis/Gaussian smoothing/clustering evaluation/clustering analysis分类
信息技术与安全科学引用本文复制引用
彭显刚,赖家文,陈奕..基于聚类分析的客户用电模式智能识别方法[J].电力系统保护与控制,2014,(19):68-73,6.基金项目
广东省自然科学基金(10151009001000045)；南方电网科技项目(K-GD2012-214) This work is supported by Natural Science Foundation of Guangdong Province (No.10151009001000045) （10151009001000045）
