Residential electricity users classification based on multidimensional feature analysis and dynamic weighted clustering

崔高颖; 邵雪松; 陈 霄; 储娜娜; 张娅楠

doi:10.3969/j.issn.1009-1831.2023.06.014

您当前的位置：

首页 >

文章列表页 >

Residential electricity users classification based on multidimensional feature analysis and dynamic weighted clustering

更新时间：2026-03-05

- Residential electricity users classification based on multidimensional feature analysis and dynamic weighted clustering
- Vol. 25, Issue 6, (2023)
- 作者机构：
  
  1. 国网江苏省电力有限公司营销服务中心,南京,210019
  2. 国网江苏省电力有限公司,南京,210014
  3. 东南大学电气工程学院,南京,210096
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2023.06.014
  CLC：
- Published：2023
- 稿件说明：
移动端阅览
崔高颖, 邵雪松, 陈霄, et al. Residential electricity users classification based on multidimensional feature analysis and dynamic weighted clustering[J]. 2023, 25(6).
DOI：

崔高颖, 邵雪松, 陈霄, et al. Residential electricity users classification based on multidimensional feature analysis and dynamic weighted clustering[J]. 2023, 25(6). DOI： 10.3969/j.issn.1009-1831.2023.06.014.

摘要

由于居民用户用电需求的高度随机性和不规则性

亟需详细的数据分析来定义用户的行为特征

以提供更加合理的用电建议和需求响应潜力。为了进一步挖掘非介入式辨识数据的价值

提出一种基于多维用电行为数据的电力居民用户分类方法。首先通过非介入式智能电表获取居民细粒度用电数据

分析用户的用电行为

寻找到关键用电特征量；接着使用CRITIC权重法自适应配置各指标权重

通过6类聚类评价指标

对4种聚类算法和3个数据距离计算进行对比

实现最优聚类方法和聚类数目的选择。通过某小区实际数据验证了本文所提用电特征量以及定权聚类方法的有效性

将居民用户群体分成用电行为差异明显的两类。

Abstract

Due to the high randomness and irregularity of residential users’electricity demand

detailed data analysis is urgently needed to define the behavior characteristics of users to provide more reasonable electricity suggestions and demand response potential. Based on the fine-grained electricity consumption data and user information of residents

a classification of electricity residential users based on multi- dimensional electricity consumption behavior data is proposed. First of all

the non-intrusive smart meter is used to obtain the fine-grained electricity consumption data of residents；Then the user’s electricity consumption behavior is analyzed

and electricity consumption characteristics are found. Then

the CRITIC weight method is used to adaptively configure the weights of each index

and through the evaluation indicators of 6 types of clusters

4 kinds of clustering algorithms and 3 data distance calculations are compared to achieve the optimal clustering method and the choice of the number of clusters. The actual data of a residential area are used to verify the power consumption characteristics and the effectiveness of the weight-fixing clustering method proposed in this paper

and the residential user groups are divided into two categories with obvious differences in electricity consumption behavior.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on distributionally robust optimization based group tuning control strategy for distributed photovoltaic system under solar load gap

Optimal scheduling of virtual power plant based on energy storage and conditional value at risk

Intelligent building demand response optimization strategy for flexible load and carbon trading mechanism

Research on multi state interaction capability of central air conditioning system based on data driven approach

Rapid evaluation method of large-scale air conditioning demand response potential based on simulation model library

Related Author

田闽哲

李俊楠

何心铭

郭露方

马茗婕

黄梦旗

易文飞

叶志刚

Related Institution

国网河南省电力公司营销服务中心

国网江苏省电力有限公司电力科学研究院

国网江苏省电力有限公司盐城供电公司

需求侧多能互补优化与供需互动技术北京市重点实验室（中国电力科学研究院有限公司）

华北电力大学电气与电子工程学院

AI问答

⁰