The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

TL;DR

This survey reviews the use of artificial intelligence techniques for detecting non-technical losses in electricity distribution, highlighting current methods, challenges, and future research directions.

Contribution

It provides a comprehensive overview of AI-based NTL detection methods, including algorithms, features, datasets, and identifies key challenges and future opportunities.

Findings

NTL detection can significantly reduce revenue loss.

AI methods vary in effectiveness depending on data quality.

Future research should address data scarcity and model interpretability.

Abstract

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.