CO 2 sensors for occupancy estimations: Potential in building automation applications

Gruber, Mattias; Trüschel, Anders; Dalenbäck, Jan-Olof

doi:10.1016/j.enbuild.2014.09.002

Cited by 68 publications

(38 citation statements)

References 7 publications

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“…It is shown that this extension addresses and compensates for auto-correlation in the CO2 observations that are otherwise neglected. Another way of describing the relation between CO2 and occupancy is a mass balance equation ( [2,[14][15][16]). The advantage of this methodology lies in the direct physical interpretation of the model.…”

Section: Related Workmentioning

confidence: 99%

CO₂-based grey-box model to estimate airflow rate and room occupancy

Wolf

Alonso²,

Calı̀

et al. 2019

E3S Web Conf.

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In the existing building stock, heating, cooling and ventilation often run on fixed schedules assuming maximal occupancy. However, fitting the control of the HVAC system to the building’s real demand offers large potential for energy savings over the status quo. Building occupants’ presence as well as mechanically supplied and infiltrated airflow rates provide information that enables to define tailored strategies for demand-controlled ventilation. Hence, real-time estimations of these quantities are a valuable input to demand-controlled built environments. In this work, the use of stochastic differential equations (SDE) to estimate the room occupancy, infiltration air-rate and ventilation air-rate is investigated. In particular, a grey-box model based on a carbon dioxide (CO2) mass balance equation is presented. The model combines knowledge about the physical system with statistical, data-driven parameter estimation. Furthermore, the proposed model contains uncertainty parameters. This is in contrast to purely deterministic models based on ordinary differential equations, where uncertainty is usually disregarded. The suggested model has been tested in a naturally ventilated and in a mechanically ventilated environment; the performance in these two cases has been compared. We show that the ability to address measurement errors and non-homogeneous conditions in the room air implies that the suggested SDE-based grey-box approach is suitable in the context of demand-controlled ventilation.

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Section: Related Workmentioning

confidence: 99%

CO₂-based grey-box model to estimate airflow rate and room occupancy

Wolf

Alonso²,

Calı̀

et al. 2019

E3S Web Conf.

View full text Add to dashboard Cite

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“…Stupakov et al [123] Citation details: [14], for surface temperature and heat flow in historical buildings such as museums [15], carbon dioxide (CO2) sensors for determining occupancy disturbances in commercial buildings [16], demand-based supervisory temperature control sensors for measuring temperature at breathing levels in largescale rooms [17,18] or integrated sensing systems for indoor applications [19,20].…”

Section: Introductionmentioning

confidence: 99%

Indoor air quality and energy management through real-time sensing in commercial buildings

Kumar

Martani

Morawska

et al. 2016

Energy and Buildings

135

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“…As for the physics-based CO 2 models, assuming well-mixed air in the room, authors in [10] derived a bilinear model which has similarities with the model presented in this paper. Still assuming well-mixed air, [11], [12] and [5] make use of massbalance equations and linear models for the CO 2 dynamics. More detailed models are considered in [13].…”

Section: Introductionmentioning

confidence: 99%

“…The estimation of occupancy levels in buildings has important implications in efficient control of Heating, Venting and Air Conditioning (HVAC) systems and diagnostics [1], [2], [3], [4], [5], [6], [7]. Instrumenting buildings with dedicated hardware such as camera systems may raise privacy concerns and be economically disadvantageous, in particular when this requires retrofitting old structures.…”

Section: Introductionmentioning

confidence: 99%

Multi-room occupancy estimation through adaptive gray-box models

Ebadat

Bottegal

Molinari

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-We consider the problem of estimating the occupancy level in buildings using indirect information such as CO2 concentrations and ventilation levels. We assume that one of the rooms is temporarily equipped with a device measuring the occupancy. Using the collected data, we identify a gray-box model whose parameters carry information about the structural characteristics of the room. Exploiting the knowledge of the same type of structural characteristics of the other rooms in the building, we adjust the gray-box model to capture the CO2 dynamics of the other rooms. The occupancy estimators are then designed using a regularized deconvolution approach which aims at estimating the occupancy pattern that best explains the observed CO2 dynamics. We evaluate the proposed scheme through extensive simulation using a commercial software tool, IDA-ICE, for dynamic building simulation.

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CO 2 sensors for occupancy estimations: Potential in building automation applications

Cited by 68 publications

References 7 publications

CO₂-based grey-box model to estimate airflow rate and room occupancy

CO₂-based grey-box model to estimate airflow rate and room occupancy

Indoor air quality and energy management through real-time sensing in commercial buildings

Multi-room occupancy estimation through adaptive gray-box models

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CO 2 sensors for occupancy estimations: Potential in building automation applications

Cited by 68 publications

References 7 publications

CO2-based grey-box model to estimate airflow rate and room occupancy

CO2-based grey-box model to estimate airflow rate and room occupancy

Indoor air quality and energy management through real-time sensing in commercial buildings

Multi-room occupancy estimation through adaptive gray-box models

Contact Info

Product

Resources

About

CO₂-based grey-box model to estimate airflow rate and room occupancy

CO₂-based grey-box model to estimate airflow rate and room occupancy