A Method for Electricity Theft Detection Based on Markov Transition Field and Mixed Neural Network

The accurate detection of electricity theft is crucial for reducing non-technical losses in smart grids. However, many existing data-driven methods rely on a single data modality, such as either the raw 1D consumption sequence or its transformed 2D image. This single-modality approach may not fully capture the complex spatio-temporal patterns associated with fraudulent behavior. To address this limitation, this paper proposes a novel detection method that integrates Markov Transition Fields (MTFs) with a hybrid neural network. First, this approach uses MTF to convert 1D time-series consumption data into 2D feature images, which enhances state-transition patterns. A parallel Residual Network and Long Short-Term Memory (ResNet-LSTM) architecture is then designed to simultaneously extract global temporal features from the original 1D data and local spatial features from the MTF images, with their fused representation used for classification. Experimental validation on a real-world dataset from the State Grid Corporation of China (SGCC)—comprising 6000 users over 304 days—demonstrates the effectiveness of our approach. The proposed model achieves a detection accuracy of 94.0% on an independent test set of 1200 users, significantly outperforming several state-of-the-art single-modality benchmarks. This work provides a new technical method for intelligent electricity theft prevention system.