Systematic review of energy theft practices and autonomous detection through artificial intelligence methods

Erika Stracqualursi, Antonello Rosato, Gianfranco Di Lorenzo, Massimo Panella and Rodolfo Araneo

Renewable and Sustainable Energy Reviews, 2023, vol. 184, issue C

Abstract: Energy theft poses a significant challenge for all parties involved in energy distribution, and its detection is crucial for maintaining stable and financially sustainable energy grids. One potential solution for detecting energy theft is through the use of artificial intelligence (AI) methods. This systematic review article provides an overview of the various methods used by malicious users to steal energy, along with a discussion of the challenges associated with implementing a generalized AI solution for energy theft detection. In this work, we analyze the benefits and limitations of AI methods, including machine learning, deep learning, and neural networks, and relate them to the specific thefts also analyzing problems arising with data collection. The article proposes key aspects of generalized AI solutions for energy theft detection, such as the use of smart meters and the integration of AI algorithms with existing utility systems. Overall, we highlight the potential of AI methods to detect various types of energy theft and emphasize the need for further research to develop more effective and generalized detection systems, providing key aspects of possible generalized solutions.

Keywords: Artificial intelligence; Autonomous theft detection; Electricity theft; Energy meter tampering; Machine learning; Non-technical losses; Smart grids (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S136403212300401X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:184:y:2023:i:c:s136403212300401x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2023.113544

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().