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Electricity Theft Detection Techniques
TopicLeading institutions, researchers & key papers
This cluster of papers focuses on the detection and prevention of electricity theft in smart grids, particularly through the use of advanced metering infrastructure, machine learning, deep learning, and anomaly detection techniques. The research explores methods such as support vector machines, decision trees, convolutional neural networks, and feature engineering to address non-technical losses and improve the security of electricity distribution systems.
26
Works
IDs:OpenAlex
How has Electricity Theft Detection Techniques's publication output changed over time?
ScholarIQpublication output · 2018–2023
Output grew0% over the shown period — from 1 works in 2018 to 1 in 2023.
1
2
1
1
1
20182019202020222023
What are the most-cited papers on Electricity Theft Detection Techniques?
ScholarIQmost cited works
Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan
Muhammad Salman Saeed, Mohd Wazir Mustafa, Usman Ullah Sheikh, Touqeer Ahmed Jumani, Nayyar Hussain Mirjat
Electronics. 201990 CitationsOPEN ACCESS
Machine learning approach of detecting anomalies and forecasting time-series of IoT devices
Amer Malki, El‐Sayed Atlam, Ibrahim Gad
S2764413287. 202270 CitationsOPEN ACCESS
Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review
Muhammad Salman Saeed, Mohd Wazir Mustafa, Nawaf N. Hamadneh, Nawa Alshammari, Usman Ullah Sheikh, Touqeer Ahmed Jumani, S.N. Khalid, Ilyas Khan
S198098182. 202067 CitationsOPEN ACCESS
Data-driven intelligent method for detection of electricity theft
Junde Chen, Yaser A. Nanehkaran, Weirong Chen, Yajun Liu, Defu Zhang
S168377509. 202334 Citations
A multi-tier architecture for data analytics in smart metering systems
Juan Carlos Olivares, Enrique Reyes‐Archundia, José Antonio Gutiérrez Gnecchi, Johan W. González-Murueta, Jaime Cerda-Jacobo
S114739668. 201927 Citations
Where is Electricity Theft Detection Techniques research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
Funder breakdown is a member featureSign up free to unlock
How much of the research on Electricity Theft Detection Techniques is open access?
ScholarIQopen access share
50%OPEN ACCESS
Gold
50%
Green
0%
Hybrid
0%
Bronze
0%
Closed
50%
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