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Abstract
The rapidly expansion of Internet of Things (IoT) has ignited renewed interest in energy disaggregation via nonintrusive load monitoring (NILM). Compared to the more frequent NILM approach of training one model for each appliance, this paper proposes a multi-label learning approach based on the widely cited sequence2point convolutional neural network (CNN). Using the smart meter readings collected in an office building, we demonstrate the accuracy and practicality of the proposed network compared to start-of-the-art one-to-one NILM models.
Original language | English |
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Number of pages | 5 |
Publication status | Published - 22 Sept 2021 |
Event | Fourth International Balkan Conference on Communications and Networking - Novi Sad, Serbia Duration: 20 Sept 2021 → 22 Sept 2021 Conference number: 4 http://www.balkancom.info/2021/ |
Conference
Conference | Fourth International Balkan Conference on Communications and Networking |
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Abbreviated title | Balkancom 2021 |
Country/Territory | Serbia |
City | Novi Sad |
Period | 20/09/21 → 22/09/21 |
Internet address |
Keywords
- non-intrsuive
- load monitoring
- multi-objects
- smart building
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Dive into the research topics of 'Non-intrusive load monitoring for multi-objects in smart building'. Together they form a unique fingerprint.Projects
- 1 Finished
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SENSIBLE: SENSors and Intelligence in BuiLt Environment (SENSIBLE) MSCA RISE
Stankovic, L. (Principal Investigator), Glesk, I. (Co-investigator), Gleskova, H. (Co-investigator) & Stankovic, V. (Co-investigator)
European Commission - Horizon Europe + H2020
1/01/17 → 31/12/20
Project: Research