Analysis of power large user segmentation based on Affinity propagationand K.means algorithm

魏小曼; 余昆; 陈星莺; 颜拥; 张爽; 景伟强

doi:10.3969/j.issn.1009-1831.2018.01.005

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Analysis of power large user segmentation based on Affinity propagationand K.means algorithm

更新时间：2026-03-05

- Analysis of power large user segmentation based on Affinity propagationand K.means algorithm
- Vol. 20, Issue 1, (2018)
- 作者机构：
  
  1. 河海大学能源与电气学院,南京,211100
  2. 国网浙江省电力公司电力科学研究院,杭州,310000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2018.01.005
  CLC：
- Published：2018
- 稿件说明：
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魏小曼, 余昆, 陈星莺, et al. Analysis of power large user segmentation based on Affinity propagationand K.means algorithm[J]. 2018, 20(1).
DOI：

魏小曼, 余昆, 陈星莺, et al. Analysis of power large user segmentation based on Affinity propagationand K.means algorithm[J]. 2018, 20(1). DOI： 10.3969/j.issn.1009-1831.2018.01.005.

摘要

力大用户是供电企业的重点用户，其在电力市场环境下的潜在价值以及发展趋势与供电企业的利益密切相关。为了识别出有价值的大用户行为与价值特征，提出基于近邻传播( Affinity propagation

AP)和 K.means算法相结合的电力大用户细分方法。首先，从现有指标中提取出关键细分指标，并考虑电力大用户近期和长期的电费增长率，提出评估大用户发展潜力的细分指标；其次，将 AP和 K.means 2种算法相结合，基于细分指标对电力大用户进行细分，以解决寻找初始聚类中心以及聚类数的问题，同时避免 K.means聚类时容易陷入局部最优的问题；算例分析以浙江某地区的用户数据为基础进行算例分析，验证所提出的电力大用户细分方法切实可行。

Abstract

Power large users are the key users of power sup.ply enterprises and their potential value and development trend inpower market environment are closely related to the profit of powersupply enterprises.In order to identify valuable user behavior andvalue characteristics

a large user segmentation method based on APand K.means algorithm is proposed.First of all

from the existing in.dicators to extract the key sub.indicators

and consider the recentlarge and long.term power consumption rate of electricity

put for.ward to assess the development potential of large users of the break.down of indicators.Secondly

the AP and K.means are combined tosegment the large power users to solve the problem of finding the ini.tial clustering center and the number of clusters.At the same time

itis easy to avoid the local optimization when K.means clustering prob.lem; finally

based on the user data of a region in Zhejiang province

the author analyzes the feasibility of the proposed method.

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