Double-layer stacking optimization for electricity theft detection considering data incompleteness and intra-class imbalance
Electricity theft detection is crucial for reducing non-technical losses in electric power enterprises and ensuring fairness in electric power transactions. However, challenges such as incomplete data, a scarcity of electricity theft samples, and limited performance of detection systems hinder accur...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525000122 |
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| Summary: | Electricity theft detection is crucial for reducing non-technical losses in electric power enterprises and ensuring fairness in electric power transactions. However, challenges such as incomplete data, a scarcity of electricity theft samples, and limited performance of detection systems hinder accurate identification of theft. To address these issues, this paper presents a method utilizing a two-stage time-series generative adversarial network (TimeGAN) with an integrated two-layer stacking optimization configuration. The first phase tackles data incompleteness through an enhanced version of TimeGAN, employing embedding and recovery layers to reconstruct incomplete power user data. It introduces an analog denoising training method and supervised information assistance to improve the interpolation accuracy. In the second stage, the method addresses inter- and intra-class imbalances in electricity theft detection by employing the K-shape clustering algorithm to identify unique patterns within the theft data. This enables balanced synthesis of theft samples using these patterns as conditional supervisory terms for TimeGAN. To concurrently optimize the combination of the electricity theft detector (ETD) model and its hyperparameters, an integrated two-layer Stacking optimization framework is developed. This framework incorporates a time-varying binary transfer function and an external repository to enhance the performance of the optimization algorithm. Simulation tests performed on 42,372 actual power consumption records demonstrated that the proposed method achieved, on average, 8.23% higher DR scores, 3.45% higher AUC scores, and 5.60% higher Macro-F1 scores compared to the baseline method. |
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| ISSN: | 0142-0615 |