Regularized Deconvolution-Based Approaches for Estimating Room Occupancies

Abstract

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. Note to Practitioners-Home automation systems benefit from automatic recognition of human presence in the built environment.Since dedicated hardware is costly, it may be preferable to detect occupancy with software-based systems which do not require the installation of additional devices. The object of this study is the reconstruction of occupancy patterns in a room using measurements of concentration, temperature, fresh air inflow, and door opening/closing events. All these signals are information sources often available in HVAC systems of modern buildings and homes. We assess the value of such information sources in terms of their relevance in detecting occupancy in small and medium-sized rooms. The proposed estimation scheme is composed of two distinct phases. The first is a training phase where the goal is to de-Manuscript

Indoor occupancy estimation from carbon dioxide concentration

“…A recent work identified a linear dynamic model of the indoor temperature and CO2 concentration in which the occupancy level is an input [3]. With such a dynamic model, the number of occupants can be estimated by solving a deconvolution problem.…”

“…The experimental results verify the effectiveness of the proposed estimator. To the best of our knowledge, most of the current work deals with very few occupants [3], [33], [34], and no non-intrusive and non-terminal-based method has been shown to be effective for rooms with more than 20 occupants.…”

“…In [3], [37]- [41] the indoor CO2 concentration is assumed to be uniform, and in [37]- [40] the length of time horizon l = 1, which implies that indoor CO2 concentration is with Markov property (memoryless property ). However, [42] showed that the gradient of indoor CO2 concentration can be very large, and CO2 emitted by certain occupant cannot be immediately sensed.…”

“…Apparently, if the CO2 dynamic model has been identified, the number of indoor occupants can be estimated based on the real-time CO2 measurements. In [3], the estimator is designed by solving a deconvolution problem. In this strategy, the deconvolution process suffers from truncation errors and the error of the identified CO2 dynamic model.…”

“…I NDOOR occupancy information is an important part of home and office automation. It can be used as an input for the control of indoor lighting systems [1], [2] and Heat, Ventilation and Air-conditioning (HVAC) systems [3], [4]. Studies have shown that around one-third of the energy consumed in buildings can be saved using occupancy-based control [5]- [9].…”