Abstract:The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity theft detection, which focuses on electricity consumption-side behavior, PV generation fraud detection (PVG-FD) is complicated by the inherent intermittency and uncertainty of PV generation. The distributed nature of PV systems poses further challenges for centralized PVG-FD approaches due to scalability and privacy concerns. This paper develops a privacy-preserving distributed PVG-FD framework based on federated learning (FL). In this framework, a utility company manages multiple household communities, where each of which is equipped with a local detector. The framework integrates a novel detection model architecture with privacy-preserving global collaboration. Each community's local model fuses PV generation and weather data via a co-attention mechanism to detect discrepancies critical for PVG-FD. The FL framework enables cross-community collaboration by aggregating model parameters and prototypes, leveraging global knowledge sharing with local refinement while preserving privacy. It also uses prototype alignment to address class imbalance by enhancing fraud sample representation. Extensive experiments on a real-world residential PV dataset validate the effectiveness of the developed method and demonstrate that it outperforms state-of-the-art FL methods across various scenarios. The results also show its scalability across varying community sizes and strong robustness to class imbalance.
| Comments: | 15 pages |
| Subjects: | Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2605.17039 [cs.LG] |
| (or arXiv:2605.17039v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.17039 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | IEEE Transactions on Smart Grid, 2026 |
| Related DOI: | https://doi.org/10.1109/TSG.2026.3692585
DOI(s) linking to related resources |
From: Hao Wang [view email]
[v1]
Sat, 16 May 2026 15:19:14 UTC (1,603 KB)
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