Analysisofusers’electricitybehaviorandinfluencingfactorsbasedonclustering

李顺昕; 远振海; 丁健民; 岳云力; 邓春宇; 刘凤魁; 张玉天; 王新迎

doi:10.3969/j.issn.1009-1831.2019.03.012

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Analysisofusers’electricitybehaviorandinfluencingfactorsbasedonclustering

更新时间：2026-03-05

- Analysisofusers’electricitybehaviorandinfluencingfactorsbasedonclustering
- Vol. 21, Issue 3, (2019)
- 作者机构：
  
  1. 国网冀北电力有限公司经济技术研究院,北京,100038
  2. 中国电建集团租赁有限公司,北京,100160
  3. 中国电力科学研究院有限公司,北京,100192
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2019.03.012
  CLC：
- Published：2019
- 稿件说明：
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李顺昕, 远振海, 丁健民, et al. Analysisofusers’electricitybehaviorandinfluencingfactorsbasedonclustering[J]. 2019, 21(3).
DOI：

李顺昕, 远振海, 丁健民, et al. Analysisofusers’electricitybehaviorandinfluencingfactorsbasedonclustering[J]. 2019, 21(3). DOI： 10.3969/j.issn.1009-1831.2019.03.012.

摘要

随着我国产业结构的不断调整，用户的用电特性也不断变化，且用户的用电行为逐渐向个性化发展。首先利用离散小波变换对用户负荷进行特征提取；然后利用改进快速密度峰值聚类算法进行聚类，根据用户特征聚类结果的不同，将用户分为不同群组，分析负荷群的时间分布特征；采用互信息方法分析用电量数据与经济、气温、行业关键指标等的相关性，提取出关键影响因素；最后，基于某省某行业典型用户的仿真实例验证了本文所提方法的有效性。

Abstract

With the constant adjustment of the industrial structure in China

the users’characteristics are changing

and the users’electricity behavior gradually develops into individuation.Firstly

the discrete wavelet transform is used to extract the characteristics of user load data. Secondly

the improved fast density peaks clustering algorithm is used to cluster the users into load groups with different power consumption behaviors

and then the time distribution characteristics of the load groups are analyzed.The mutual information method is used to analyze the correlation between electricity consumption data

economy

temperature

industry key indicators and so on

and the key influencing factors are extracted. Finally

the simulation results of a typical user in an industry in a province verify the effectiveness of the proposed method.

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