Abstract: In smart grids, electricity theft is still a major problem that causes large financial losses and inefficiencies in operations. Because of their complexity and size, traditional theft detection techniques like manual inspections and rule-based algorithms are unable to handle the complexity and size of contemporary smart grids. In order to detect electricity theft, this research explores the use of deep neural networks (DNNs), which are able to evaluate massive datasets and recognize complex patterns linked to fraudulent activity. We provide a thorough process that covers data preparation, feature extraction, model architecture design, training, and evaluation for creating a DNN-based theft detection system. The suggested approach outperforms traditional techniques, giving utilities a reliable tool to improve theft detection and preserve grid integrity.

| DOI: 10.17148/IARJSET.2024.11743