Self-Supervised Latent Feature-Guided Multi-Step Diffusion Model for Electricity Theft Detection With Imbalanced and Missing Data

Abstract

The widespread adoption of advanced metering infrastructure has provided abundant data, enabling the integration of deep learning techniques into smart grids. However, it has also led to more sophisticated and concealed methods of electricity theft. Due to the challenges posed by data imbalance and missing values caused by device malfunctions and communication issues, existing deep learning models often perform poorly. To address these issues, this paper proposes a multi-step training framework named DING, which incorporates diffusion generation, self-supervised pre-training, normalized condition imputation, and generation-balanced fine-tuning. First, sufficient balanced smart meter data is generated using a diffusion model. Second, a pre-trained encoder is trained on the generated data, extracting unbiased low-dimensional features that can be used for downstream classification tasks and as conditions to guide the training of the imputation model. Next, an imputation model is trained based on a diffusion state-space equation. Finally, fine-tuning is performed on the balanced data. Experiments on a real dataset from the State Grid Corporation of China demonstrate that the proposed method outperforms previous models for both electricity theft detection and imputation tasks.