A Robust Approach to Electricity Theft Detection via Tensor Representation-Driven Contrastive Distillation

Electricity theft in smart grids has become an increasingly serious issue. Existing detection methods are often limited by their ability to learn from high-dimensional features and by resource constraints, resulting in suboptimal performance. Moreover, incomplete data caused by real-world factors, such as advanced metering infrastructure (AMI) communication interruptions or sensor failures further complicates the detection task. To address these challenges, we propose a tensor representation-driven contrastive distillation model for robust electricity theft detection. Specifically, we first employ tensor decomposition to dynamically capture multidimensional spatio-temporal features from grid data, enabling the model to learn complex temporal dependencies and better represent missing information. We further introduce a semi-supervised learning framework that integrates knowledge distillation and contrastive learning to enhance the detection capability of lightweight models, especially under conditions of limited data quality and incomplete data scenarios in dynamic environments. Finally, we formulate a multitask learning objective that harmonizes discriminative classification with representation coherence. This synergistic interplay enhances the model’s robustness in complex consumption scenarios and sharpens its ability to identify suspicious behaviors. Extensive experiments demonstrate that the proposed model outperforms state-of-the-art methods.