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Estimation of building occupancy levels through environmental signals deconvolution
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-1927-1690
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2013 (English)In: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, 2013Conference paper, Published paper (Refereed)
Abstract [en]

We address the problem of estimating the occupancy levelsin rooms using the information available in standardHVAC systems. Instead of employing dedicated devices, weexploit the significant statistical correlations between the occupancylevels and the CO2 concentration, room temperature,and ventilation actuation signals in order to identify adynamic model. The building occupancy estimation problemis formulated as a regularized deconvolution problem, wherethe estimated occupancy is the input that, when injected intothe identified model, best explains the currently measuredCO2 levels. Since occupancy levels are piecewise constant,the zero norm of occupancy is plugged into the cost functionto penalize non-piecewise constant inputs. The problemthen is seen as a particular case of fused-lasso estimator byrelaxing the zero norm into the `1 norm. We propose bothonline and offline estimators; the latter is shown to performfavorably compared to other data-based building occupancyestimators. Results on a real testbed show that the MSE ofthe proposed scheme, trained on a one-week-long dataset, is half the MSE of equivalent Neural Network (NN) or SupportVector Machine (SVM) estimation strategies.

Place, publisher, year, edition, pages
2013.
Keyword [en]
System Identification, Parametric and Nonparametric methods, Inference
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-136440DOI: 10.1145/2528282.2528290ISBN: 978-1-4503-2431-1 (print)OAI: oai:DiVA.org:kth-136440DiVA: diva2:676176
Conference
BuildSys 2013 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings; Rome, Italy, 13-14 November, 2013
Note

QC 20140219

Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2014-02-19Bibliographically approved

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Wahlberg, BoJohansson, Karl H.

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Ebadat, AfroozBottegal, GiulioVaragnolo, DamianoWahlberg, BoJohansson, Karl H.
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