Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things

WANG Ying; XIANG Wen; ZHANG Qun; GAO Xiuyun

doi:10.15938/j.jhust.2022.02.014

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Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things

更新时间：2026-08-31

- Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things
- Vol. 27, Issue 2, Pages: 106-113(2022)
- 作者机构：
  
  1. 1哈尔滨工程大学智能科学与工程学院,哈尔滨,150001
  2. 3国网黑龙江省电力有限公司经济技术研究院,哈尔滨,150036
  3. 2东北农业大学电气与信息学院,哈尔滨,150038
- 作者简介：
- 基金信息：
- DOI：10.15938/j.jhust.2022.02.014
  CLC： TP301
- Published：2022
- 稿件说明：
移动端阅览
WANG Ying, XIANG Wen, ZHANG Qun, et al. Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things[J]. 2022, 27(2): 106-113.
DOI：

WANG Ying, XIANG Wen, ZHANG Qun, et al. Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things[J]. 2022, 27(2): 106-113. DOI： 10.15938/j.jhust.2022.02.014.

摘要

摘要:在泛在电力物联网的建设中，电力企业针对客户的用电行为进行分析是必不可少的。在以往的研究中，k均值聚类算法是常用的客户用电行为分析方法之一，然而由于初始质心采用随机选择的方式，使得其容易陷入局部最优且难以收敛到全局最小值。针对该问题，提出了基于改进的动态粒子群算法优化的K-means算法（DPSO-Kmeans），并将其用于客户用电行为的分析中。在实验中，通过对312个家庭用户的用电消费行为记录进行聚类分析，结果证明DPSO-Kmeans相对于传统的Kmeans算法具有更好的聚类效果，能够提取更为典型的客户用电行为模式。

Abstract

In the construction of the ubiquitous power Internet of Things

it is indispensable to analyze customers′ electricity consumption behavior for power companies. In previous studies

the K-means clustering algorithm is one of the commonly used methods for analyzing customer electricity consumption behavior. However

because the initial centroid is randomly selected

it is easy to fall into a local optimum and difficult to converge to a global minimum. To this problem

an improved K-means algorithm (DPSO-Kmeans) based on an improved dynamic particle swarm optimization algorithm is proposed and used in the analysis of customers′ electricity consumption behavior. In the experiment

the electricity consumption behavior records of 312 household users were used for cluster analysis. The results prove that DPSO-Kmeans has a better clustering effect than the traditional K-means algorithm

and can extract more typical customers′ electrical behavior pattern.

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