A Review on Electricity Theft Detection Using Deep Neural Networks: A Comprehensive Review

The escalating challenges posed by non-technical losses in power distribution systems have prompted significant research attention from both academic and industrial perspectives. Electricity theft, constituting a substantial portion of these losses, results in considerable financial implications for utility companies worldwide, with estimates suggesting annual losses exceeding $58.7 billion globally. This review paper examines the state-of-the-art methodologies employing deep neural networks for detecting electricity theft across smart grid infrastructure. We systematically analyze architectures including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and hybrid frameworks that combine multiple deep learning paradigms. The paper synthesizes recent empirical findings, discusses inherent challenges such as class imbalance and incomplete datasets, and evaluates performance metrics across diverse geographical contexts. Our examination reveals that contemporary deep learning approaches achieve detection accuracies ranging from 90% to 98%, significantly outperforming traditional machine learning methods. We identify critical gaps in existing research, highlight promising directions for future investigation, and provide practical insights for implementation in real-world smart grid environments.