[HTML][HTML] Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review
A Pazderin, F Kamalov, PY Gubin, M Safaraliev… - Energies, 2023 - mdpi.com
Nontechnical losses of electrical energy (NTLEE) have been a persistent issue in both the
Russian and global electric power industries since the end of the 20th century. Every year …
Russian and global electric power industries since the end of the 20th century. Every year …
AI Techniques in Detection of NTLs: A Comprehensive Review
R Yadav, M Yadav, Ranvijay, Y Sawle… - … Methods in Engineering, 2024 - Springer
In the operation of power grid, worldwide, non-technical losses (NTLs) occur in a massive
amount of proportion which is observed up to 40% of the total electric transmission and …
amount of proportion which is observed up to 40% of the total electric transmission and …
An attention-based wide and deep CNN with dilated convolutions for detecting electricity theft considering imbalanced data
R Xia, Y Gao, Y Zhu, D Gu, J Wang - Electric Power Systems Research, 2023 - Elsevier
For the increasingly serious phenomenon of electricity theft, many researchers are trying to
detect it. Traditional detection methods rely on physical inspection, which has low detection …
detect it. Traditional detection methods rely on physical inspection, which has low detection …
[HTML][HTML] Detecting nontechnical losses in smart meters using a MLP-GRU deep model and augmenting data via theft attacks
The current study uses a data-driven method for Nontechnical Loss (NTL) detection using
smart meter data. Data augmentation is performed using six distinct theft attacks on benign …
smart meter data. Data augmentation is performed using six distinct theft attacks on benign …
Using machine learning ensemble method for detection of energy theft in smart meters
Electricity theft is a primary concern for utility providers, as it leads to substantial financial
losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model …
losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model …
[HTML][HTML] Deep learning-based meta-learner strategy for electricity theft detection
Electricity theft damages power grid infrastructure and is also responsible for huge revenue
losses for electric utilities. Integrating smart meters in traditional power grids enables real …
losses for electric utilities. Integrating smart meters in traditional power grids enables real …
[HTML][HTML] A deep learning technique Alexnet to detect electricity theft in smart grids
N Khan, M Amir Raza, D Ara, S Mirsaeidi… - Frontiers in Energy …, 2023 - frontiersin.org
Electricity theft (ET), which endangers public safety, creates a problem with the regular
operation of grid infrastructure and increases revenue losses. Numerous machine learning …
operation of grid infrastructure and increases revenue losses. Numerous machine learning …
A self-decision ant colony clustering algorithm for electricity theft detection
The load data features of some electricity-theft consumers during the theft period are similar
to those of normal consumers, making these electricity-theft consumers outliers from the …
to those of normal consumers, making these electricity-theft consumers outliers from the …
Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities
The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to
use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those …
use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those …