Research on portrait method of residential users based on multi⁃dimensional fine⁃grained behavior data

徐涛; 黄莉; 李敏蕾; 朱明杰

doi:10.3969/j.issn.1009-1831.2019.03.011

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Research on portrait method of residential users based on multi⁃dimensional fine⁃grained behavior data

更新时间：2026-03-05

- Research on portrait method of residential users based on multi⁃dimensional fine⁃grained behavior data
- Vol. 21, Issue 3, (2019)
- 作者机构：
  
  1. 国网江苏省电力有限公司苏州供电分公司,江苏,苏州,215000
  2. 东南大学,南京,210096
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2019.03.011
  CLC：
- Published：2019
- 稿件说明：
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徐涛, 黄莉, 李敏蕾, et al. Research on portrait method of residential users based on multi⁃dimensional fine⁃grained behavior data[J]. 2019, 21(3).
DOI：

徐涛, 黄莉, 李敏蕾, et al. Research on portrait method of residential users based on multi⁃dimensional fine⁃grained behavior data[J]. 2019, 21(3). DOI： 10.3969/j.issn.1009-1831.2019.03.011.

摘要

以电网供需互动为目标，基于非入户终端的细粒度用电行为量测数据及营销系统的网络行为统计数据，开展居民用户画像方法研究。从用户行为、用电特性、消费习惯三大维度建立用户多源特征标签体系，并提出各个特征标签的萃取方法；基于欧式距离和曼哈顿距离提出改进K均值聚类算法，并应用此方法进行电力客户总体调控簇别分析，作为互动目标用户精准定位的依据；应用特征标签体系及总体调控簇别的划分结果，对居民用户进行综合画像及可视化呈现。最后以苏州金鸡湖示范区的1 500户居民用户进行画像及应用效果分析。

Abstract

Aiming at the interaction between supply and demand of power grid

based on fine grained electricity consumption behavior measurement data collected by non household terminals and network behavior statistics of marketing system

the research on resident user portrait method is carried out. A user multi?source feature label system is established from three dimensions: user behavior

power consumption characteristics and consumption habits

and extraction methods of each feature label is proposed. Based on Euclidean distance and Manhattan distance

an improved k means clustering algorithm is proposed

and the improved k means clustering algorithm is used to divide the overall control clusters of power customers as the basis for precise positioning of target users.The system of feature labels and the results of the overall control cluster partition are used to synthetically portray and visualize the users. At last

1 500 residential users in Jinji Lake demonstration area of Suzhou were used to analyze their portraits and applications.

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