Fed‐NILM: A federated learning‐based non‐intrusive load monitoring method for privacy‐protection-Reference-Cited by-同舟云学术

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Fed‐NILM: A federated learning‐based non‐intrusive load monitoring method for privacy‐protection

Published:2022-04 Issue:2 Volume:3 Page:51-60
ISSN:2634-1581
Container-title:Energy Conversion and Economics
language:en
Short-container-title:Energy Conversion and Econom

Author:

Wang Haijin¹²,Si Caomingzhe³²,Liu Guolong¹²,Zhao Junhua¹²^ORCID,Wen Fushuan⁴²,Xue Yusheng⁵

Affiliation:

1. The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

2. Institute of Artificial Intelligence and Robotics for Society Shenzhen China

3. Department of Electrical Engineering Tsinghua University Beijing China

4. Department of Electrical Engineering Zhejiang University Hangzhou China

5. State Grid Electric Power Research Institute Nanjing China

Publisher

Institution of Engineering and Technology (IET)

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/enc2.12055

Reference32 articles.

1. Feedback on household electricity consumption: a tool for saving energy?

2. Nonintrusive appliance load monitoring

3. Weiss M. Helfenstein A. Mattern F. Staake T.:Leveraging smart meter data to recognize home appliances. In:Proceedings of the 2012 IEEE International Conference on Pervasive Computing and Communications.Lugano Switzerland(2012)

4. Liao J. Elafoudi G. Stankovic L. Stankovic V.:Nonintrusive appliance load monitoring using low‐resolution smart meter data. In:2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).Aachen Germany(2014)

5. Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Perfednilm: a practical personalized federated learning-based non-intrusive load monitoring;Industrial Artificial Intelligence;2024-04-16

2. VI Trajectory Generalization Across Smart Meter Edge Clients using Federated Learning;2024 16th International Conference on Computer and Automation Engineering (ICCAE);2024-03-14

3. DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring;Renewable and Sustainable Energy Reviews;2024-03

4. Multi-Energy Load Forecasting in Integrated Energy Systems: A Spatial-Temporal Adaptive Personalized Federated Learning Approach;IEEE Transactions on Industrial Informatics;2024

5. Advancing Power System Services With Privacy-Preserving Federated Learning Techniques: A Review;IEEE Access;2024

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