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NILM for Commercial Buildings: Deep Neural Networks Tackling Nonlinear and Multi-Phase Loads

Kulathilaka, M.J.S. and Saravanan, S. and Kumarasiri, H.D.H.P. and LOGEESHAN, V. and KUMARAWADU, S. and Rajakaruna Wanigasekara, Chathura (2024) NILM for Commercial Buildings: Deep Neural Networks Tackling Nonlinear and Multi-Phase Loads. Energies, 17 (15). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/en17153802. ISSN 1996-1073.

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Official URL: https://www.mdpi.com/1996-1073/17/15/3802


Item URL in elib:https://elib.dlr.de/205661/
Document Type:Article
Title:NILM for Commercial Buildings: Deep Neural Networks Tackling Nonlinear and Multi-Phase Loads
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kulathilaka, M.J.S.University of MoratuwaUNSPECIFIEDUNSPECIFIED
Saravanan, S.University of MoratuwaUNSPECIFIEDUNSPECIFIED
Kumarasiri, H.D.H.P.University of MoratuwaUNSPECIFIEDUNSPECIFIED
LOGEESHAN, V.University of MoratuwaUNSPECIFIEDUNSPECIFIED
KUMARAWADU, S.University of MoratuwaUNSPECIFIEDUNSPECIFIED
Rajakaruna Wanigasekara, ChathuraChathura.Wanigasekara (at) dlr.dehttps://orcid.org/0000-0003-4371-6108165828234
Date:August 2024
Journal or Publication Title:Energies
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:17
DOI:10.3390/en17153802
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:1996-1073
Status:Published
Keywords:non-intrusive load monitoring; deep neural network; WaveNet; ensemble learning; nonlinear loads
HGF - Research field:other
HGF - Program:other
HGF - Program Themes:other
DLR - Research area:no assignment
DLR - Program:no assignment
DLR - Research theme (Project):no assignment
Location: Bremerhaven
Institutes and Institutions:Institute for the Protection of Maritime Infrastructures > Reslience of Maritime Systems
Deposited By: Rajakaruna Wanigasekara, Chathura
Deposited On:20 Aug 2024 09:55
Last Modified:12 Sep 2024 09:51

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