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dc.contributor.author
Becker, Vincent
dc.contributor.author
Kleiminger, Wilhelm
dc.contributor.author
Coroamă, Vlad C.
dc.contributor.author
Mattern, Friedemann
dc.date.accessioned
2018-11-01T15:37:06Z
dc.date.available
2018-11-01T14:33:13Z
dc.date.available
2018-11-01T15:37:06Z
dc.date.issued
2018-10-10
dc.identifier.isbn
2520-8942
en_US
dc.identifier.other
10.1186/s42162-018-0022-6
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/300628
dc.identifier.doi
10.3929/ethz-b-000300628
dc.description.abstract
Because space heating causes a large fraction of energy consumed in households, occupancy-based heating systems have become more and more popular in recent years. However, there is still no practical method to estimate the potential energy savings before installing such a system. While substantial work has been done on occupancy detection, previous work does not address a combination with heating simulation in order to provide an easily applicable method to estimate this savings potential. In this paper we present such a combination of an occupancy detection algorithm based on smart electricity meter data and a building heating simulation, which only requires publicly available weather data and some relevant building characteristics. We apply our method to a dataset containing such data for several thousand households and show that when taking occupancy into account, a household can save over 9% heating energy on average, while certain groups, such as employed single-person households, can even save 14% on average. Using our approach, households with high potential for energy savings can be quickly identified and their inhabitants could be more easily convinced to adopt an occupancy-based heating strategy.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer Open
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Smart heating
en_US
dc.subject
Occupancy detection
en_US
dc.subject
Household heating simulation
en_US
dc.subject
Energy savings
en_US
dc.subject
Smart energy
en_US
dc.title
Estimating the savings potential of occupancy-based heating strategies
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.book.title
Proceedings of the 7th DACH+ Conference on Energy Informatics
en_US
ethz.journal.title
Energy Informatics
ethz.journal.volume
1
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
35
en_US
ethz.pages.end
54
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
London, England
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02658 - Institut für Pervasive Computing / Institute for Pervasive Computing::03528 - Mattern, Friedemann / Mattern, Friedemann
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02658 - Institut für Pervasive Computing / Institute for Pervasive Computing::03528 - Mattern, Friedemann / Mattern, Friedemann
en_US
ethz.date.deposited
2018-11-01T14:33:28Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-11-01T15:37:21Z
ethz.rosetta.lastUpdated
2019-01-03T09:10:33Z
ethz.rosetta.versionExported
true
ethz.COinS
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