Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
Juan Ignacio Guerrero, Carlos León, Íñigo Monedero, et al.
Knowledge-Based Systems (2014) Vol. 71, pp. 376-388
Open Access | Times Cited: 79

Showing 1-25 of 79 citing articles:

Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
Zibin Zheng, Yatao Yang, Xiangdong Niu, et al.
IEEE Transactions on Industrial Informatics (2017) Vol. 14, Iss. 4, pp. 1606-1615
Closed Access | Times Cited: 624
Review of non-technical loss detection methods
George M. Messinis, Nikos Hatziargyriou
Electric Power Systems Research (2018) Vol. 158, pp. 250-266
Closed Access | Times Cited: 200
Electricity Theft Detection in Power Grids with Deep Learning and Random Forests
LI Shuan, Yinghua Han, Xu Yao, et al.
Journal of Electrical and Computer Engineering (2019) Vol. 2019, pp. 1-12
Open Access | Times Cited: 186
Hybrid Deep Neural Networks for Detection of Non-Technical Losses in Electricity Smart Meters
Madalina-Mihaela Buzau, Javier Tejedor-Aguilera, Pedro Cruz-Romero, et al.
IEEE Transactions on Power Systems (2019) Vol. 35, Iss. 2, pp. 1254-1263
Closed Access | Times Cited: 176
Drift-Aware Methodology for Anomaly Detection in Smart Grid
Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia
IEEE Access (2019) Vol. 7, pp. 9645-9657
Open Access | Times Cited: 160
Detection Methods in Smart Meters for Electricity Thefts: A Survey
Xiaofang Xia, Yang Xiao, Wei Liang, et al.
Proceedings of the IEEE (2022) Vol. 110, Iss. 2, pp. 273-319
Closed Access | Times Cited: 75
Emotion AI at Work: Implications for Workplace Surveillance, Emotional Labor, and Emotional Privacy
Kat Roemmich, Florian Schaub, Nazanin Andalibi
(2023), pp. 1-20
Open Access | Times Cited: 46
Solutions for detection of non-technical losses in the electricity grid: A review
Joaquim L. Viegas, Paulo R. Esteves, Rui Melício, et al.
Renewable and Sustainable Energy Reviews (2017) Vol. 80, pp. 1256-1268
Open Access | Times Cited: 153
Review of various modeling techniques for the detection of electricity theft in smart grid environment
Tanveer Ahmad, Huanxin Chen, Jiangyu Wang, et al.
Renewable and Sustainable Energy Reviews (2017) Vol. 82, pp. 2916-2933
Closed Access | Times Cited: 112
NTL Detection in Electric Distribution Systems Using the Maximal Overlap Discrete Wavelet-Packet Transform and Random Undersampling Boosting
Nelson Fabian Avila, Gerardo Figueroa, Chia‐Chi Chu
IEEE Transactions on Power Systems (2018) Vol. 33, Iss. 6, pp. 7171-7180
Closed Access | Times Cited: 107
Unsupervised non-technical losses identification through optimum-path forest
Leandro A. Passos, Caio C. O. Ramos, Douglas Rodrigues, et al.
Electric Power Systems Research (2016) Vol. 140, pp. 413-423
Open Access | Times Cited: 101
Non-technical loss analysis and prevention using smart meters
Tanveer Ahmad
Renewable and Sustainable Energy Reviews (2017) Vol. 72, pp. 573-589
Closed Access | Times Cited: 91
Non-technical losses: A systematic contemporary article review
Fernando de Souza Savian, Júlio Cezar Mairesse Siluk, Taís Bisognin Garlet, et al.
Renewable and Sustainable Energy Reviews (2021) Vol. 147, pp. 111205-111205
Closed Access | Times Cited: 73
Electricity-theft detection for smart grid security using smart meter data: A deep-CNN based approach
Ejaz Ul Haq, Can Pei, Ruihong Zhang, et al.
Energy Reports (2022) Vol. 9, pp. 634-643
Open Access | Times Cited: 48
A Novel Unsupervised Data-Driven Method for Electricity Theft Detection in AMI Using Observer Meters
Ruobin Qi, Jun Zheng, Zhirui Luo, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-10
Open Access | Times Cited: 47
Fraud Detection in Energy Consumption: A Supervised Approach
Bernat Coma-Puig, Josep Carmona, Ricard Gavaldà, et al.
(2016)
Open Access | Times Cited: 80
Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid
Xiaoquan Lu, Yu Zhou, Zhongdong Wang, et al.
Energies (2019) Vol. 12, Iss. 18, pp. 3452-3452
Open Access | Times Cited: 56
Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review
Muhammad Salman Saeed, Mohd Wazir Mustafa, Nawaf N. Hamadneh, et al.
Energies (2020) Vol. 13, Iss. 18, pp. 4727-4727
Open Access | Times Cited: 51
Two-Step Electricity Theft Detection Strategy Considering Economic Return Based on Convolutional Autoencoder and Improved Regression Algorithm
Xueyuan Cui, Shengyuan Liu, Zhenzhi Lin, et al.
IEEE Transactions on Power Systems (2021) Vol. 37, Iss. 3, pp. 2346-2359
Closed Access | Times Cited: 48
A review of distribution network applications based on smart meter data analytics
Christos L. Athanasiadis, Theofilos A. Papadopoulos, Georgios C. Kryonidis, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 191, pp. 114151-114151
Closed Access | Times Cited: 22
A review of non-technical loss attack models and detection methods in the smart grid
Maria Gabriel Chuwa, Fei Wang
Electric Power Systems Research (2021) Vol. 199, pp. 107415-107415
Closed Access | Times Cited: 34
Detection of electricity theft using data processing and LSTM method in distribution systems
Behçet Kocaman, Vedat Tümen
Sadhana (2020) Vol. 45, Iss. 1
Closed Access | Times Cited: 33
Enhancing anomaly detection accuracy and interpretability in low-quality and class imbalanced data: A comprehensive approach
Bixuan Gao, Xiangyu Kong, Shangze Li, et al.
Applied Energy (2023) Vol. 353, pp. 122157-122157
Closed Access | Times Cited: 12
Cyberattack detection for electricity theft in smart grids via stacking ensemble GRU optimization algorithm using federated learning framework
Jun Wang, Yifei Si, Yonghai Zhu, et al.
International Journal of Electrical Power & Energy Systems (2024) Vol. 157, pp. 109848-109848
Open Access | Times Cited: 4
RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids
Aqsa Khalid, Ghulam Mustafa, Muhammad Rizwan Rashid Rana, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e1872-e1872
Open Access | Times Cited: 4
Page 1 - Next Page

Scroll to top