Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things
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 beh...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2022-04-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2082 |
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| author | WANG Ying XIANG Wen ZHANG Qun GAO Xiuyun |
| author_facet | WANG Ying XIANG Wen ZHANG Qun GAO Xiuyun |
| author_sort | WANG Ying |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2ae02ccf7d2a400bb8cbec0b8a0dabf8 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2022-04-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-2ae02ccf7d2a400bb8cbec0b8a0dabf82025-08-20T03:34:13ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832022-04-01270210611310.15938/j.jhust.2022.02.014Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of ThingsWANG Ying0XIANG Wen1ZHANG Qun2GAO Xiuyun3College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China;Economic and Technological Research Institute of State Grid, Heilongjiang Electric Power Co., Ltd., Harbin 150036,ChinaCollege of Electrical and Information, Northeast Agriculture University, Harbin, 150038, China;Economic and Technological Research Institute of State Grid, Heilongjiang Electric Power Co., Ltd., Harbin 150036,ChinaEconomic and Technological Research Institute of State Grid, Heilongjiang Electric Power Co., Ltd., Harbin 150036,ChinaEconomic and Technological Research Institute of State Grid, Heilongjiang Electric Power Co., Ltd., Harbin 150036,ChinaIn 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.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2082analysis of electricity consumptionk-means clustering algorithminitial centroiddynamic particle swarm algorithmelectricity usage behavior model |
| spellingShingle | WANG Ying XIANG Wen ZHANG Qun GAO Xiuyun Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things Journal of Harbin University of Science and Technology analysis of electricity consumption k-means clustering algorithm initial centroid dynamic particle swarm algorithm electricity usage behavior model |
| title | Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things |
| title_full | Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things |
| title_fullStr | Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things |
| title_full_unstemmed | Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things |
| title_short | Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things |
| title_sort | analysis of customer power consumption behavior based on dpso kmeans under the ubiquitous power internet of things |
| topic | analysis of electricity consumption k-means clustering algorithm initial centroid dynamic particle swarm algorithm electricity usage behavior model |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2082 |
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