Power supply demand division method for end users based on big data mining

胡丹蕾; 赵 冬; 姜世公; 王云飞; 张重阳; 柳 伟

doi:10.3969/j.issn.1009-1831.2023.04.011

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Power supply demand division method for end users based on big data mining

更新时间：2026-03-05

- Power supply demand division method for end users based on big data mining
- Vol. 25, Issue 4, (2023)
- 作者机构：
  
  1. 国网经济技术研究院有限公司,北京,102209
  2. 南京理工大学自动化学院,南京,210094
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2023.04.011
  CLC：
- Published：2023
- 稿件说明：
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胡丹蕾, 赵冬, 姜世公, et al. Power supply demand division method for end users based on big data mining[J]. 2023, 25(4).
DOI：

胡丹蕾, 赵冬, 姜世公, et al. Power supply demand division method for end users based on big data mining[J]. 2023, 25(4). DOI： 10.3969/j.issn.1009-1831.2023.04.011.

摘要

针对不同供电区域多类型终端用户对供电可靠性的差异化需求以及目前配网侧依据单一供电可靠率指标衡量用户供电可靠性的局限性，提出一种基于大数据挖掘的终端用户的供电需求划分方法。首先，进行终端用户差异化供电需求量化，构建终端用户供电需求信息模型；然后，采用综合K-means 与基于密度聚类（density- based spatial clustering ofapplications with noise，DBSCAN）的大数据挖掘进行终端用户的聚类，实现用户的分类；最后采用改进的灰色关联度实现区域内终端用户可靠性等级划分。通过对含多类型终端用户的供电区域仿真分析，结合对比方案分析，进一步验证所提出的基于大数据挖掘的终端用户的供电可靠性需求划分方法的有效性。

Abstract

In view of different demands for power supply reliability of multi-type end users in different regions and the limitation of measuring user power supply reliability based on a single power supply reliability index at the distribution network side

a power supply demand division method for end users based on big data mining is proposed. Firstly

the differentiated power supply demand of endusers is quantified

and the information model of end-users’powersupply demand is constructed. Then

using integrated K-means and density- based spatial clustering of applications with noise big data mining to cluster end users

to achieve user classification. Finally

improved grey relational degree is used to divide the end user reliability level within the region. Through the simulation analysis of the power supply area with multi-type end users

combined with the analysis of comparative schemes

the validity of the proposed method based on big data mining is further verified.

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