Research on FCM-LR cross electricity theft detection based on big data user profile

Ronghui Hu () and Tong Zhen
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Ronghui Hu: Henan Vocational College of Information and Statistics
Tong Zhen: Henan University of Technology

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 7, No 31, 3265 pages

Abstract: 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%.

Keywords: Electricity theft detection (ETD); Fuzzy c-means and logistic regression cross detection (FCM-LR); Imbalance; User profile (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-024-02333-8

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