Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.
- Schäuble, Dominik & Marian, Adela & Cremonese, Lorenzo, 2020. "Conditions for a cost-effective application of smart thermostat systems in residential buildings," Applied Energy, Elsevier, vol. 262(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- İsmail Hakkı Çavdar & Vahit Feryad, 2021. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid," Energies, MDPI, vol. 14(15), pages 1-21, July.
- Apostolos Vavouris & Benjamin Garside & Lina Stankovic & Vladimir Stankovic, 2022. "Low-Frequency Non-Intrusive Load Monitoring of Electric Vehicles in Houses with Solar Generation: Generalisability and Transferability," Energies, MDPI, vol. 15(6), pages 1-27, March.
- Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring," Energies, MDPI, vol. 15(9), pages 1-20, May.
- Große-Kreul, Felix, 2022. "What will drive household adoption of smart energy? Insights from a consumer acceptance study in Germany," Utilities Policy, Elsevier, vol. 75(C).
- Qingwei Shi & Hong Ren & Weiguang Cai & Jingxin Gao, 2020. "How to Set the Proper CO 2 Reduction Targets for the Provincial Building Sector of China?," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
- Everton Luiz de Aguiar & André Eugenio Lazzaretti & Bruna Machado Mulinari & Daniel Rodrigues Pipa, 2021. "Scattering Transform for Classification in Non-Intrusive Load Monitoring," Energies, MDPI, vol. 14(20), pages 1-20, October.
- Todic, Tamara & Stankovic, Vladimir & Stankovic, Lina, 2023. "An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem," Applied Energy, Elsevier, vol. 341(C).
- Li, Dandan & Li, Jiangfeng & Zeng, Xin & Stankovic, Vladimir & Stankovic, Lina & Xiao, Changjiang & Shi, Qingjiang, 2023. "Transfer learning for multi-objective non-intrusive load monitoring in smart building," Applied Energy, Elsevier, vol. 329(C).
- Ma, Minda & Ma, Xin & Cai, Wei & Cai, Weiguang, 2020. "Low carbon roadmap of residential building sector in China: Historical mitigation and prospective peak," Applied Energy, Elsevier, vol. 273(C).
- Li, Chuyi & Zheng, Kedi & Guo, Hongye & Chen, Qixin, 2023. "A mixed-integer programming approach for industrial non-intrusive load monitoring," Applied Energy, Elsevier, vol. 330(PA).
- He, Xianya & Lin, Jian & Xu, Jinmei & Huang, Jingzhi & Wu, Nianyuan & Zhang, Yining & Liu, Songling & Jing, Rui & Xie, Shan & Zhao, Yingru, 2023. "Long-term planning of wind and solar power considering the technology readiness level under China's decarbonization strategy," Applied Energy, Elsevier, vol. 348(C).
- Fabio Gualandri & Aleksandra Kuzior, 2023. "Home Energy Management Systems Adoption Scenarios: The Case of Italy," Energies, MDPI, vol. 16(13), pages 1-20, June.
- Andreas Reinhardt & Lucas Pereira, 2021. "Special Issue: “Energy Data Analytics for Smart Meter Data”," Energies, MDPI, vol. 14(17), pages 1-3, August.
- Thomaßen, Georg & Kavvadias, Konstantinos & Jiménez Navarro, Juan Pablo, 2021. "The decarbonisation of the EU heating sector through electrification: A parametric analysis," Energy Policy, Elsevier, vol. 148(PA).
- Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano & Saad Dosse Bennani & Hakim El Fadili, 2022. "Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection," Energies, MDPI, vol. 15(3), pages 1-22, February.
- Hao Ma & Juncheng Jia & Xinhao Yang & Weipeng Zhu & Hong Zhang, 2021. "MC-NILM: A Multi-Chain Disaggregation Method for NILM," Energies, MDPI, vol. 14(14), pages 1-14, July.
More about this item
Keywords
load disaggregation; neural NILM; federated learning; energy recommender systems;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:991-:d:1037197. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.