Blind identification strategies for room occupancy estimation
2015 (English)Conference paper (Refereed)
We propose and test on real data a two-tier estimation strategy for inferring occupancy levels from measurements of CO2 concentration and temperature levels. The first tier is a blind identification step, based either on a frequentist Maximum Likelihood method, implemented using non-linear optimization, or on a Bayesian marginal likelihood method, implemented using a dedicated Expectation-Maximization algorithm. The second tier resolves the ambiguity of the unknown multiplicative factor, and returns the final estimate of the occupancy levels. The overall procedure addresses some practical issues of existing occupancy estimation strategies. More specifically, first it does not require the installation of special hardware, since it uses measurements that are typically available in many buildings. Second, it does not require apriori knowledge on the physical parameters of the building, since it performs system identification steps. Third, it does not require pilot data containing measured real occupancy patterns (i.e., physically counting people for some periods, a typically expensive and time consuming step), since the identification steps are blind.
Place, publisher, year, edition, pages
System identification, management of HVAC systems, Maximum Likelihood, Expectation-Maximization
IdentifiersURN: urn:nbn:se:kth:diva-165326ScopusID: 2-s2.0-84963813624OAI: oai:DiVA.org:kth-165326DiVA: diva2:808037
European Control Conference, July 15-17 2015, Linz
QC 201508262015-04-272015-04-272015-08-26Bibliographically approved