Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multi-room occupancy estimation through adaptive gray-box models
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0003-0283-5717
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
Division of Signals and Systems, Department of Computer Science, Electrical and Space Engineering, Luleå University of Innovation and Technology.
Show others and affiliations
2015 (English)In: Decision and Control (CDC), 2015 IEEE 54th Annual Conference on, IEEE conference proceedings, 2015, 3705-3711 p.Conference paper, Published paper (Other academic)
Abstract [en]

We consider the problem of estimating the occupancylevel in buildings using indirect information such as CO2 concentrations and ventilation levels. We assume that oneof the rooms is temporarily equipped with a device measuringthe occupancy. Using the collected data, we identify a gray-boxmodel whose parameters carry information about the structuralcharacteristics of the room. Exploiting the knowledge of thesame type of structural characteristics of the other rooms inthe building, we adjust the gray-box model to capture the CO2dynamics of the other rooms. Then the occupancy estimatorsare designed using a regularized deconvolution approach whichaims at estimating the occupancy pattern that best explainsthe observed CO2 dynamics. We evaluate the proposed schemethrough extensive simulation using a commercial software tool,IDA-ICE, for dynamic building simulation.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 3705-3711 p.
Keyword [en]
Occupancy estimation, Maximum Likelihood, CO2 dynamics, inference, building automation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-178171DOI: 10.1109/CDC.2015.7402794ISI: 000381554503143Scopus ID: 2-s2.0-84962030285OAI: oai:DiVA.org:kth-178171DiVA: diva2:877684
Conference
IEEE Conference on Decision and Control (CDC),15-18 Dec. 2015, Osaka, Japan
Note

QC 20160212

Available from: 2015-12-07 Created: 2015-12-07 Last updated: 2016-12-20Bibliographically approved

Open Access in DiVA

fulltext(421 kB)92 downloads
File information
File name FULLTEXT01.pdfFile size 421 kBChecksum SHA-512
d72dc17748df4e82b1bc9d8469c4ede4c0188b22f5fccfa20de2aa1035fc3faef51e2fd75ffa9540d1b9dd48b8123b74fd7ff87e71d458626ecda3eb43041133
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Ebadat, AfroozBottegal, GiulioMolinari, MarcoWahlberg, BoHjalmarsson, HåkanJohansson, Karl Henrik
By organisation
Automatic ControlACCESS Linnaeus Centre
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 92 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 107 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf