AI-Based Electricity Theft Detection System: A Comparative Performance Analysis of Machine Learning and Deep Learning Models

Abstract

Electricity theft is a worldwide challenge that causes significant losses for utility companies, with annual revenue losses estimated at USD 89.3 billion globally. The widespread rollout of Advanced Metering Infrastructure (AMI) and smart meters has made it easier to access large amounts of high-frequency consumption data from consumers. This access allows companies to use data-driven strategies for electricity theft detection. This review looks at how well machine learning (ML) and deep learning (DL) models detect electricity theft (ETD) using three benchmark datasets. The datasets presented are the State Grid Corporation of China (SGCC) dataset, the Open Energy Data Initiative Theft Detection Dataset 2022 (OEDI/TDD2022), and the Irish Commission for Energy Regulation Smart Metering Project (CER SMP) dataset. A thorough evaluation of 27 models is conducted, covering classical machine learning classifiers, tree ensemble methods, anomaly detection techniques, recurrent neural networks, convolutional networks, Transformer-based encoders, and federated learning systems. Key preprocessing methods, including filling in missing values, normalization, feature engineering, and tackling class imbalance with SMOTE and LoRAS, are noted for each model. The findings show that deep learning methods consistently outperform traditional machine learning techniques across all three benchmark datasets. The CNN-LSTM model, improved with LoRAS, achieves the top reported F1-score of 97.7% on the SGCC dataset. Meanwhile, the Federated ConvGRU model reaches the highest accuracy of 97.8% on OEDI/TDD2022. Transformer architectures show the best overall performance in average categories, with a mean accuracy of 95.8% and a mean F1-score of 95.5%. Addressing class imbalance is seen as the most influential factor, showing an 18.7 percentage-point difference in F1 scores between unaugmented and augmented LSTM models on SGCC. This review concludes that combining hybrid augmentation-driven deep learning architectures with privacy-preserving federated learning frameworks offers the most scalable and practical way to design Electricity theft detection (ETD) systems. The research community should prioritize standardized cross-dataset benchmarking and mandatory reporting of computational complexity as immediate priorities.