Performance-Aware NILM Model Optimization for Edge Deployment-Reference-Cited by-同舟云学术

Performance-Aware NILM Model Optimization for Edge Deployment

Published:2023-09 Issue:3 Volume:7 Page:1434-1446
ISSN:2473-2400
Container-title:IEEE Transactions on Green Communications and Networking
language:
Short-container-title:IEEE Trans. on Green Commun. Netw.

Author:

Sykiotis Stavros¹^ORCID,Athanasoulias Sotirios¹^ORCID,Kaselimi Maria¹^ORCID,Doulamis Anastasios¹^ORCID,Doulamis Nikolaos¹^ORCID,Stankovic Lina²^ORCID,Stankovic Vladimir²^ORCID

Affiliation:

1. School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Athens, Greece

2. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.K.

Funder

European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Renewable Energy, Sustainability and the Environment

Link

http://xplorestaging.ieee.org/ielx7/7511293/10224556/10042478.pdf?arnumber=10042478

Reference58 articles.

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3. Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning

4. Regularized LSTM Based Deep Learning Model: First Step towards Real-Time Non-Intrusive Load Monitoring

5. Non-Intrusive Load Disaggregation Using Graph Signal Processing

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