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

arXiv: https://arxiv.org/abs/1606.00626 · HTML: https://mlsanity.com/paper/1606.00626/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids](https://mlsanity.com/paper/2102.06039/) (2021)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [A Novel DDPM-based Ensemble Approach for Energy Theft Detection in Smart Grids](https://mlsanity.com/paper/2307.16149/) (2023)

## Abstract (arXiv)

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.