Electricity Theft Detection Techniques Using Artificial Intelligence: A Survey

Electricity theft poses significant challenges to utility companies, resulting in revenue losses, increased operational costs, and compromised safety. Traditional methods of detecting electricity theft often fall short in accurately identifying instances of illegal consumption. Leveraging artificial intelligence (AI) techniques has emerged as a promising approach to enhance the efficiency and effectiveness of electricity theft detection systems. This paper proposes a survey that explores the landscape of AI-powered techniques utilized in the detection of electricity theft. We categorize these techniques into various methodologies such as machine learning, deep learning, data mining and data analytics. By analyzing a wide range of research papers, industry reports, and case studies, we provide insights into the strengths, limitations, and practical implications of each approach. Furthermore, we investigate the key challenges associated with implementing AI-based electricity theft detection systems, including data privacy concerns, algorithm robustness, and real-time processing requirements. We discuss potential solutions and emerging trends aimed at overcoming these challenges and improving the reliability of detection systems. Ultimately, this survey aims to provide a comprehensive understanding of the current state-of-the-art in AI-based electricity theft detection techniques, offering valuable insights for researchers, practitioners, and policymakers in the energy sector.