A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context-Reference-Cited by-同舟云学术

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A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context

Published:2024-02 Issue: Volume:305 Page:113890
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Rafiq Hasan^ORCID,Manandhar Prajowal^ORCID,Rodriguez-Ubinas Edwin^ORCID,Ahmed Qureshi Omer,Palpanas Themis

Publisher

Elsevier BV

Reference162 articles.

1. P. Friedlingstein, et al., Global carbon budget 2022, Nov. 2022. doi: 10.5194/essd-14-4811-2022.

2. R. Lindsey, Climate change: atmospheric carbon dioxide.

3. The Paris agreement: a new beginning?;Savaresi;J. Energy Nat. Resour. Law,2016

4. IEA, CO2 Emissions in 2022, https://www.iea.org/reports/co2-emissions-in-2022.

5. IEA, Buildings, https://www.iea.org/reports/buildings.

Cited by 6 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. Non-intrusive load monitoring based on MoCo_v2, time series self-supervised learning;Energy and Buildings;2024-08

2. A Feature Engineering‐Based NILM Framework for Appliance Recognition Considering Data Class Imbalance;IEEJ Transactions on Electrical and Electronic Engineering;2024-07-18

3. UNet-WD: Deep Learning for Multi-Appliance Water Disaggregation;2024 IFIP Networking Conference (IFIP Networking);2024-06-03

4. Beyond privacy and security: Exploring ethical issues of smart metering and non-intrusive load monitoring;Telematics and Informatics;2024-06

5. Spatiotemporal Deep Learning for Power System Applications: A Survey;IEEE Access;2024

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