Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information
The construction of smart cities contributes to promoting residents’ life convenience and sustainable energy development. Despite these advancements, the challenge of fully analyzing and understanding residents’ energy usage behaviors leads to inefficient energy use and potenti...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10824781/ |
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| _version_ | 1832590310682656768 |
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| author | Wenqing Zhou Chaoqiang Chen Qin Yan Bin Li Kang Liu Yingjun Zheng Hongming Yang Hui Xiao Sheng Su |
| author_facet | Wenqing Zhou Chaoqiang Chen Qin Yan Bin Li Kang Liu Yingjun Zheng Hongming Yang Hui Xiao Sheng Su |
| author_sort | Wenqing Zhou |
| collection | DOAJ |
| description | The construction of smart cities contributes to promoting residents’ life convenience and sustainable energy development. Despite these advancements, the challenge of fully analyzing and understanding residents’ energy usage behaviors leads to inefficient energy use and potential economic losses. Current resident anomaly detection technologies rely on single-source energy data, lacking detailed behavior pattern analysis. Hence, this paper proposes a method to detect abnormal residential water and electricity usage by incorporating multi-source information. Specifically, the correlation between water and electricity usage of residential customers is analyzed based on real metering data and the use of the Copula distribution function, followed by the integration of two innovative data mining techniques to form an anomaly detection framework. The distance correlation coefficient algorithm is used to measure the relevance of users’ water and electricity usage data. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the distance correlation coefficient for users and detect abnormal users whose distance correlation coefficient curves deviate from the normal user clusters. This multi-source approach avoids single-source bias by improving the data accuracy over one-dimensional methods. Experiments are implemented in a real low-voltage transformer area to prove the validity of the proposed method. |
| format | Article |
| id | doaj-art-1d7959a6e15e4959b781ddda817141fc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1d7959a6e15e4959b781ddda817141fc2025-01-24T00:01:13ZengIEEEIEEE Access2169-35362025-01-0113122151222410.1109/ACCESS.2025.352572610824781Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption InformationWenqing Zhou0https://orcid.org/0009-0004-7157-8771Chaoqiang Chen1Qin Yan2Bin Li3https://orcid.org/0000-0003-4678-8369Kang Liu4Yingjun Zheng5https://orcid.org/0009-0009-5803-395XHongming Yang6https://orcid.org/0000-0001-6760-5918Hui Xiao7https://orcid.org/0009-0005-4726-426XSheng Su8https://orcid.org/0000-0003-3201-0193School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, ChinaState Grid of China, Changsha Electric Power Company Ltd., Changsha, ChinaSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, ChinaSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha, ChinaSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, ChinaSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, ChinaSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, ChinaSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, ChinaThe construction of smart cities contributes to promoting residents’ life convenience and sustainable energy development. Despite these advancements, the challenge of fully analyzing and understanding residents’ energy usage behaviors leads to inefficient energy use and potential economic losses. Current resident anomaly detection technologies rely on single-source energy data, lacking detailed behavior pattern analysis. Hence, this paper proposes a method to detect abnormal residential water and electricity usage by incorporating multi-source information. Specifically, the correlation between water and electricity usage of residential customers is analyzed based on real metering data and the use of the Copula distribution function, followed by the integration of two innovative data mining techniques to form an anomaly detection framework. The distance correlation coefficient algorithm is used to measure the relevance of users’ water and electricity usage data. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the distance correlation coefficient for users and detect abnormal users whose distance correlation coefficient curves deviate from the normal user clusters. This multi-source approach avoids single-source bias by improving the data accuracy over one-dimensional methods. Experiments are implemented in a real low-voltage transformer area to prove the validity of the proposed method.https://ieeexplore.ieee.org/document/10824781/Advanced metering infrastructurebehavior analysisdistance correlation coefficientmulti-source informationsmart cities |
| spellingShingle | Wenqing Zhou Chaoqiang Chen Qin Yan Bin Li Kang Liu Yingjun Zheng Hongming Yang Hui Xiao Sheng Su Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information IEEE Access Advanced metering infrastructure behavior analysis distance correlation coefficient multi-source information smart cities |
| title | Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information |
| title_full | Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information |
| title_fullStr | Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information |
| title_full_unstemmed | Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information |
| title_short | Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information |
| title_sort | anomaly usage behavior detection based on multi source water and electricity consumption information |
| topic | Advanced metering infrastructure behavior analysis distance correlation coefficient multi-source information smart cities |
| url | https://ieeexplore.ieee.org/document/10824781/ |
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