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

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.

References in corpus (4)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Semi-supervised clustering methods
• Reactive point processes: A new approach to predicting power failures in underground electrical systems
• Hawkes Processes

Cited by in corpus (6)

• Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
• Electricity Theft Detection with self-attention
• Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
• Efficient Power Theft Detection for Residential Consumers Using Mean Shift Data Mining Knowledge Discovery Process
• Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
• An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids