Occupancy-based energy consumption modelling using machine learning algorithms for institutional buildings
dc.contributor.author
Anand, Prashant
dc.contributor.author
Deb, Chirag
dc.contributor.author
Yang, Ke
dc.contributor.author
Yang, Junjing
dc.contributor.author
Cheong, David
dc.contributor.author
Sekhar, Chandra
dc.date.accessioned
2021-10-12T15:16:36Z
dc.date.available
2021-10-07T04:24:15Z
dc.date.available
2021-10-12T15:16:36Z
dc.date.issued
2021-12-01
dc.identifier.issn
0378-7788
dc.identifier.issn
1872-6178
dc.identifier.other
10.1016/j.enbuild.2021.111478
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/508529
dc.description.abstract
Occupancy information is one of the crucial variables in modelling and predicting the energy use in buildings. However, the presence of occupants is often stochastic in nature. In the presented case of eight space typologies derived from three institutional building blocks, a substantial variation in the correlation between occupancy and energy consumption is found for different space types during the semester and semester breaks for various resolutions of day and time. Further, it has been identified that a weak correlation between occupants and energy use is due to the use of common plug and lighting loads such as office printers, projectors, lab instruments, and fluorescent lamps. However, in the spaces studied, such as offices and computer rooms, the control of plug and lighting loads can be at individual occupancy levels for avoiding energy wastages. To characterize occupancy and energy consumption patterns by different space types and occupant types, this study develops and presents an integrated, data-driven modelling framework and results for different space types (like classrooms, studios, computer rooms, office spaces and laboratories and time resolution (hourly to semester-long intervals) for the case study building. It is found that the Deep Neural Network (DNN) model exhibits a slightly better prediction accuracy than conventional machine learning/regression models such as gradient boosting, support-vector network, and feed-forward neural network. However, in terms of computation time, the gradient boosting model is found to be faster than the DNN model for comparable outcomes. The developed integrated model will better equip the building facility management for an occupant-oriented control of building systems for energy efficiency.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Institutional Buildings
en_US
dc.subject
Building energy consumption
en_US
dc.subject
Space type characterization
en_US
dc.subject
Wi-Fi based Occupancy Count
en_US
dc.subject
Energy prediction
en_US
dc.subject
Machine learning
en_US
dc.subject
Deep neural network
en_US
dc.title
Occupancy-based energy consumption modelling using machine learning algorithms for institutional buildings
en_US
dc.type
Journal Article
dc.date.published
2021-09-17
ethz.journal.title
Energy and Buildings
ethz.journal.volume
252
en_US
ethz.journal.abbreviated
Energy build.
ethz.pages.start
111478
en_US
ethz.size
21 p.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-10-07T04:24:23Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-10-12T15:16:46Z
ethz.rosetta.lastUpdated
2022-03-29T14:09:51Z
ethz.rosetta.versionExported
true
ethz.COinS
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Journal Article [130370]