Convolutional sequence to sequence non-intrusive load monitoring
dc.contributor.author
Chen, Kunjin
dc.contributor.author
Wang, Qin
dc.contributor.author
He, Ziyu
dc.contributor.author
Chen, Kunlong
dc.contributor.author
Hu, Jun
dc.contributor.author
He, Jinliang
dc.date.accessioned
2019-02-15T12:03:25Z
dc.date.available
2019-01-28T15:55:20Z
dc.date.available
2019-02-15T12:03:25Z
dc.date.issued
2018-11-29
dc.identifier.other
10.1049/joe.2018.8352
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/320506
dc.identifier.doi
10.3929/ethz-b-000320506
dc.description.abstract
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this study. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partially overlapped output sequences of the network are averaged to produce the final output of the model. The authors apply the proposed model to the reference energy disaggregation data set dataset and compare it with the convolutional sequence to point model in the literature. Results show that the proposed model is able to give satisfactory disaggregation performance for appliances with varied characteristics.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Institution of Engineering and Technology
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
Convolutional sequence to sequence non-intrusive load monitoring
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
dc.date.published
2018-10-31
ethz.journal.title
The Journal of Engineering
ethz.journal.volume
2018
en_US
ethz.journal.issue
17
en_US
ethz.pages.start
1860
en_US
ethz.pages.end
1864
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
Stevenage
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02604 - Inst. für Bau- & Infrastrukturmanagement / Inst. Construction&Infrastructure Manag.::09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02604 - Inst. für Bau- & Infrastrukturmanagement / Inst. Construction&Infrastructure Manag.::09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
en_US
ethz.date.deposited
2019-01-28T15:55:27Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-02-15T12:03:32Z
ethz.rosetta.lastUpdated
2022-03-28T22:17:41Z
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