Regularized deconvolution-based approaches for estimating room occupancies

A. Ebadat, G. Bottegal, D. Varagnolo, B.G. Wahlberg, K.H. Johansson

Research output: Contribution to journalArticleAcademicpeer-review

32 Citations (Scopus)
13 Downloads (Pure)


We address the problem of estimating the number of people in a room using information available in standard HVAC systems. We propose an estimation scheme based on two phases. In the first phase, we assume the availability of pilot data and identify a model for the dynamic relations occurring between occupancy levels, concentration and room temperature. In the second phase, we make use of the identified model to formulate the occupancy estimation task as a deconvolution problem. In particular, we aim at obtaining an estimated occupancy pattern by trading off between adherence to the current measurements and regularity of the pattern. To achieve this goal, we employ a special instance of the so-called fused lasso estimator, which promotes piecewise constant estimates by including an norm-dependent term in the associated cost function. We extend the proposed estimator to include different sources of information, such as actuation of the ventilation system and door opening/closing events. We also provide conditions under which the occupancy estimator provides correct estimates within a guaranteed probability. We test the estimator running experiments on a real testbed, in order to compare it with other occupancy estimation techniques and assess the value of having additional information sources.
Original languageEnglish
Pages (from-to)1157-1168
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Issue number4
Publication statusPublished - 1 Oct 2015
Externally publishedYes


  • Deconvolution
  • occupancy estimation
  • regularization
  • system identification


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