In situ air change rate estimation from metabolic CO 2 measurements and state-space modelling

This work introduces a method for the estimation of fluctuating air change rates (ACR) in occupied buildings, using measurements of CO2 concentration. The target of a measurement-based method is to propose an alternative to airflow network simulation, which relies on many assumptions and uncertain parameters to calculate air change rates. The methodology is based on the stochastic formulation of the CO2 conservation equation, solved by the Extended Kalman Filter. It allows accounting for measurement noise, modelling uncertainty and input uncertainty, in the evaluation of the ACR. Two improvements to the base CO2 conservation inverse problem, to account for eventual high fluctuations of the ACR: the state-space model is augmented with an equation for the CO2 production rate, and the variance of the ACR may depend on the detection of window openings. The case study is a monitored experimental test house, which was also simulated in EnergyPlus, so that the method could be tested on simulated data or real measured data. Results show that the estimated ACR may be in good agreement with values calculated by airflow network simulation, under some conditions: the difference between indoor and outdoor CO2 concentration cannot be too small for ACR to be identifiable. Very high air change rates are therefore difficult to estimate, beyond a few minutes after a window is opened. The method can however be considered promising for establishing the air flow signature of monitored buildings and designing natural cooling solutions.HighlightsA method is proposed to evaluate air change rates from in-situ CO2 measurements.The Extended Kalman filter is used to estimate in realistic occupancy conditions.Data from a naturally ventilated test house are analyzed.Air change rate estimates are compared to airflow network simulations.