Research on FCM-LR cross electricity theft detection based on big data user profile
Data-driven electricity theft detection (ETD) based on machine learning and deep learning has the advantages of automation, real-time performance, and efficiency while requiring a large amount of labeled data to train models. However, the imbalance ratio between positive and unlabeled samples has re...
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| Published in | International journal of system assurance engineering and management Vol. 15; no. 7; pp. 3251 - 3265 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New Delhi
Springer India
01.07.2024
Springer Nature B.V |
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| Abstract | Data-driven electricity theft detection (ETD) based on machine learning and deep learning has the advantages of automation, real-time performance, and efficiency while requiring a large amount of labeled data to train models. However, the imbalance ratio between positive and unlabeled samples has reached 1:200, which significantly limits the accuracy of the ETD model. In cases like this, we refer to it as positive-unlabeled learning. Down-sampling wastes a large amount of negative samples, while up-sampling will result in the ETD model not being robust. Both can lead to ETD models performing well in experimental environments but poorly in production environments. In this context, this paper proposes a semi-supervised electricity theft detection algorithm based on fuzzy c-means and logistic regression cross detection (FCM-LR). Firstly, a statistical feature set based on business data and load data is proposed to depict the profile of electricity users, which can achieve the effect of reducing the complexity of data structure. Furthermore, by using the FCM-LR method, the utilization of unlabeled data can be maximized, and new electricity theft patterns can be discovered. The simulation results show that the theft detection effect of this method is significant, with Precision, Recall, F1, and Area under Curve all approaching 99%. |
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| AbstractList | Data-driven electricity theft detection (ETD) based on machine learning and deep learning has the advantages of automation, real-time performance, and efficiency while requiring a large amount of labeled data to train models. However, the imbalance ratio between positive and unlabeled samples has reached 1:200, which significantly limits the accuracy of the ETD model. In cases like this, we refer to it as positive-unlabeled learning. Down-sampling wastes a large amount of negative samples, while up-sampling will result in the ETD model not being robust. Both can lead to ETD models performing well in experimental environments but poorly in production environments. In this context, this paper proposes a semi-supervised electricity theft detection algorithm based on fuzzy c-means and logistic regression cross detection (FCM-LR). Firstly, a statistical feature set based on business data and load data is proposed to depict the profile of electricity users, which can achieve the effect of reducing the complexity of data structure. Furthermore, by using the FCM-LR method, the utilization of unlabeled data can be maximized, and new electricity theft patterns can be discovered. The simulation results show that the theft detection effect of this method is significant, with Precision, Recall, F1, and Area under Curve all approaching 99%. |
| Author | Zhen, Tong Hu, Ronghui |
| Author_xml | – sequence: 1 givenname: Ronghui orcidid: 0009-0003-7815-7323 surname: Hu fullname: Hu, Ronghui email: WilliamHuYang@outlook.com organization: Henan Vocational College of Information and Statistics – sequence: 2 givenname: Tong surname: Zhen fullname: Zhen, Tong organization: College of Information Science and Engineering, Henan University of Technology |
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| ContentType | Journal Article |
| Copyright | The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Fuzzy c-means and logistic regression cross detection (FCM-LR) User profile Imbalance Electricity theft detection (ETD) |
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| SubjectTerms | Algorithms Big Data Data structures Deep learning Electricity Engineering Engineering Economics Logistics Machine learning Marketing Organization Original Article Quality Control Real time Reliability Safety and Risk Sampling Statistical analysis Theft |
| Title | Research on FCM-LR cross electricity theft detection based on big data user profile |
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