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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| 主要な著者: | , , , |
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| フォーマット: | 論文 |
| 言語: | 中国語 |
| 出版事項: |
Harbin University of Science and Technology Publications
2022-04-01
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| シリーズ: | Journal of Harbin University of Science and Technology |
| 主題: | |
| オンライン･アクセス: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2082 |
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| 要約: | 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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| ISSN: | 1007-2683 |