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OpenAlex Citation Counts

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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:

A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing
Hasan Rafiq, Xiaohan Shi, Hengxu Zhang, et al.
Energies (2020) Vol. 13, Iss. 9, pp. 2195-2195
Open Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

NILM applications: Literature review of learning approaches, recent developments and challenges
Georgios-Fotios Angelis, Christos Timplalexis, Stelios Krinidis, et al.
Energy and Buildings (2022) Vol. 261, pp. 111951-111951
Closed Access | Times Cited: 119
A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context
Hasan Rafiq, Prajowal Manandhar, Edwin Rodríguez-Ubiñas, et al.
Energy and Buildings (2024) Vol. 305, pp. 113890-113890
Closed Access | Times Cited: 17
Review on Deep Neural Networks Applied to Low-Frequency NILM
Patrick Huber, Alberto Calatroni, Andreas Rumsch, et al.
Energies (2021) Vol. 14, Iss. 9, pp. 2390-2390
Open Access | Times Cited: 83
Sequence to point learning based on bidirectional dilated residual network for non-intrusive load monitoring
Ziyue Jia, Linfeng Yang, Zhenrong Zhang, et al.
International Journal of Electrical Power & Energy Systems (2021) Vol. 129, pp. 106837-106837
Open Access | Times Cited: 67
Generalizability Improvement of Deep Learning-Based Non-Intrusive Load Monitoring System Using Data Augmentation
Hasan Rafiq, Xiaohan Shi, Hengxu Zhang, et al.
IEEE Transactions on Smart Grid (2021) Vol. 12, Iss. 4, pp. 3265-3277
Closed Access | Times Cited: 66
An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem
Tamara Todic, Vladimir Stanković, Lina Stanković
Applied Energy (2023) Vol. 341, pp. 121078-121078
Open Access | Times Cited: 36
A novel carbon emission estimation method based on electricity‑carbon nexus and non-intrusive load monitoring
Yingqi Xia, Gengchen Sun, Yanfeng Wang, et al.
Applied Energy (2024) Vol. 360, pp. 122773-122773
Closed Access | Times Cited: 10
Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection
Inoussa Habou Laouali, A.E. Ruano, M.G. Ruano, et al.
Energies (2022) Vol. 15, Iss. 3, pp. 1215-1215
Open Access | Times Cited: 22
Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters
Sakib Mahmud, Fayçal Bensaali, Muhammad E. H. Chowdhury, et al.
Building and Environment (2025), pp. 112635-112635
Open Access
New parallel hybrid PHCNN-GRU deep learning model for multi-output NILM disaggregation
Jamila Ouzine, Manal Marzouq, Saad Dosse Bennani, et al.
Energy Efficiency (2025) Vol. 18, Iss. 3
Closed Access
Review on Deep Neural Networks Applied to Low-Frequency NILM
Patrick Huber, Alberto Calatroni, Andreas Rumsch, et al.
(2021)
Open Access | Times Cited: 30
Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network
Jiateng Song, Hongbin Wang, Mingxing Du, et al.
Energies (2021) Vol. 14, Iss. 3, pp. 684-684
Open Access | Times Cited: 27
Blockchain-Based Clustered Federated Learning for Non-Intrusive Load Monitoring
Tianjing Wang, Zhaoyang Dong
IEEE Transactions on Smart Grid (2023) Vol. 15, Iss. 2, pp. 2348-2361
Closed Access | Times Cited: 9
A semi-supervised load identification method with class incremental learning
Leixin Qiu, Tao Yu, Chaofan Lan
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107768-107768
Closed Access | Times Cited: 3
Research on non-intrusive load decomposition model based on parallel multi-scale attention mechanism and its application in smart grid
Guobing Pan, Haipeng Wang, 桃代 郷田, et al.
Energy and Buildings (2024) Vol. 312, pp. 114210-114210
Closed Access | Times Cited: 3
MEMS: An automated multi-energy management system for smart residences using the DD-LSTM approach
Liao Ji-xiang, Dawei Yang, Noreen Izza Arshad, et al.
Sustainable Cities and Society (2023) Vol. 98, pp. 104850-104850
Closed Access | Times Cited: 8
Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning
Md. Tanvir Ahammed, Md. Mehedi Hasan, Md Shamsul Arefin, et al.
IEEE Access (2021) Vol. 9, pp. 115053-115067
Open Access | Times Cited: 18
An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules
Weiyue Xu, Changhao Jiang, Qihang Zhang, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 8, pp. 086210-086210
Closed Access | Times Cited: 2
Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion
Hari Prasad Devarapalli, D. V. S. S. Siva Sarma, Sitarama B. Gunturi
Energies (2020) Vol. 13, Iss. 18, pp. 4628-4628
Open Access | Times Cited: 16
Machine Learning Techniques for Non-Intrusive Load Monitoring for Enhanced Energy Predictions
Tushar Chugh, Kanishka Tyagi, Rolly Seth
2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (2024), pp. 0322-0328
Closed Access | Times Cited: 1
Non-Intrusive Load State Monitoring Through Smart Meter ‎Disaggregation Using 1d Deep ‎Reconstruction Networks
Sakib Mahmud, Mahdi Houchati, Fayçal Bensaali, et al.
(2024)
Closed Access | Times Cited: 1
A Study of the Effects of Appliance Energy Signatures on Different Neural Network Types in Nonintrusive Load Monitoring
Madhawa Herath, Chitral J. Angammana, Migara H. Liyanage
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-10
Closed Access | Times Cited: 4
Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks
Inoussa Habou Laouali, I.L.R. Gomes, M.G. Ruano, et al.
Energies (2022) Vol. 15, Iss. 23, pp. 9073-9073
Open Access | Times Cited: 6
Power Profile and Thresholding Assisted Multi-Label NILM Classification
Muhammad Asif Ali Rehmani, Saad Aslam, Shafiqur Rahman Tito, et al.
Energies (2021) Vol. 14, Iss. 22, pp. 7609-7609
Open Access | Times Cited: 6
Conv-NILM-Net, a Causal and Multi-appliance Model for Energy Source Separation
Mohamed Alami C., Jérémie Decock, Rim Kaddah, et al.
Communications in computer and information science (2023), pp. 207-222
Open Access | Times Cited: 2
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