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

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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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Abbreviations

ETD:

Electricity theft detection

PUL:

Positive-unlabeled learning

FCM-LR:

Fuzzy c-means and logistic regression cross detection

TL:

Technical loss

NTL:

Non-technical loss

SGCC:

State grid corporation of China

AMI:

Advanced measuring infrastructure

SG186:

SG186 marketing system

SMOTE:

Synthetic minority over-sampling technique

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Authors and Affiliations

  1. Henan Vocational College of Information and Statistics, Henan, 450000, China

    Ronghui Hu

  2. College of Information Science and Engineering, Henan University of Technology, Henan, 450001, China

    Tong Zhen

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  1. Ronghui Hu
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  2. Tong Zhen
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Correspondence to Ronghui Hu.

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Hu, R., Zhen, T. Research on FCM-LR cross electricity theft detection based on big data user profile. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02333-8

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