Electricity theft detection in integrated energy systems considering multi-energy loads
The significant progress has been made in electricity theft detection, but most classic works focus on electricity theft detection in residential environments, neglecting other locations such as hotels, industrial plants, and street lights. Moreover, these works typically limit their scope to power...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524006525 |
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| author | Wenlong Liao Dechang Yang Leijiao Ge Yixiong Jia Zhe Yang |
| author_facet | Wenlong Liao Dechang Yang Leijiao Ge Yixiong Jia Zhe Yang |
| author_sort | Wenlong Liao |
| collection | DOAJ |
| description | The significant progress has been made in electricity theft detection, but most classic works focus on electricity theft detection in residential environments, neglecting other locations such as hotels, industrial plants, and street lights. Moreover, these works typically limit their scope to power systems alone, without considering heating and cooling systems. To this end, this paper aims to discuss the electricity theft detection in integrated energy systems where industrial plants are typically categorized. Firstly, we conduct a theoretical, qualitative, and quantitative analysis of the correlation between multi-energy loads (i.e., electrical, heating, and cooling loads), which provides insights into the motivation for considering these correlations in electricity theft detection. After that, multi-energy loads are projected into graphs where adjacency matrices represent their correlation and feature matrices denote their consumption readings. Furthermore, a Chebyshev graph convolutional network (ChebGCN) is proposed to detect malicious users by capturing latent features and correlations from the graphs. Simulation results demonstrate that the incorporation of heating and cooling loads can significantly enhance the performance of various machine learning models for electricity theft detection. Additionally, the detection performance of the proposed ChebGCN is consistently better than both classical and state-of-the-art machine learning models, no matter whether the fraud rate of the dataset is low or high. |
| format | Article |
| id | doaj-art-a5d7a6cf036e4e279ca55eca961c58cf |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-a5d7a6cf036e4e279ca55eca961c58cf2025-01-19T06:24:00ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110428Electricity theft detection in integrated energy systems considering multi-energy loadsWenlong Liao0Dechang Yang1Leijiao Ge2Yixiong Jia3Zhe Yang4Wind Engineering and Renewable Energy Laboratory, Ecole Polytechnique Federale de Lausanne, Lausanne 1015, SwitzerlandCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Corresponding AuthorKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaDepartment of Electrical and Electronic Engineering, The University of Hong Kong, Hong KongDepartment of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, United KingdomThe significant progress has been made in electricity theft detection, but most classic works focus on electricity theft detection in residential environments, neglecting other locations such as hotels, industrial plants, and street lights. Moreover, these works typically limit their scope to power systems alone, without considering heating and cooling systems. To this end, this paper aims to discuss the electricity theft detection in integrated energy systems where industrial plants are typically categorized. Firstly, we conduct a theoretical, qualitative, and quantitative analysis of the correlation between multi-energy loads (i.e., electrical, heating, and cooling loads), which provides insights into the motivation for considering these correlations in electricity theft detection. After that, multi-energy loads are projected into graphs where adjacency matrices represent their correlation and feature matrices denote their consumption readings. Furthermore, a Chebyshev graph convolutional network (ChebGCN) is proposed to detect malicious users by capturing latent features and correlations from the graphs. Simulation results demonstrate that the incorporation of heating and cooling loads can significantly enhance the performance of various machine learning models for electricity theft detection. Additionally, the detection performance of the proposed ChebGCN is consistently better than both classical and state-of-the-art machine learning models, no matter whether the fraud rate of the dataset is low or high.http://www.sciencedirect.com/science/article/pii/S0142061524006525False Data InjectionElectricity TheftAdvanced Metering InfrastructureDeep LearningIntegrated Energy Systems |
| spellingShingle | Wenlong Liao Dechang Yang Leijiao Ge Yixiong Jia Zhe Yang Electricity theft detection in integrated energy systems considering multi-energy loads International Journal of Electrical Power & Energy Systems False Data Injection Electricity Theft Advanced Metering Infrastructure Deep Learning Integrated Energy Systems |
| title | Electricity theft detection in integrated energy systems considering multi-energy loads |
| title_full | Electricity theft detection in integrated energy systems considering multi-energy loads |
| title_fullStr | Electricity theft detection in integrated energy systems considering multi-energy loads |
| title_full_unstemmed | Electricity theft detection in integrated energy systems considering multi-energy loads |
| title_short | Electricity theft detection in integrated energy systems considering multi-energy loads |
| title_sort | electricity theft detection in integrated energy systems considering multi energy loads |
| topic | False Data Injection Electricity Theft Advanced Metering Infrastructure Deep Learning Integrated Energy Systems |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524006525 |
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