A Comprehensive Systematic Meta-Survey of Energy Theft Detection: From Traditional Methods to Generative AI

Abstract

Electrical energy theft presents a serious and significant challenge for utility companies worldwide. It poses substantial risks to energy infrastructure, reduces efficiency, and destabilizes distribution networks. The introduction of the Advanced Metering Infrastructure (AMI) in the last decade has significantly boosted the development of new methods and techniques for detecting electrical energy theft. This advancement is primarily due to the availability of electric consumption and other data with greater granularity (e.g., every 15 min), which has enabled the development of analytic and data-driven approaches as opposed to mass inspections alone. In this context, this paper aims to analyze the landscape of energy theft detection by providing a systematic analysis of the state-of-the-art manuscripts in the form of a tertiary study (i.e., a review of literature reviews and surveys) in accordance with the PRISMA methodology guidelines. Consequently, a comparative framework is presented, along with well-formulated research questions designed to explore the past, present, and future directions of energy theft detection.