Modelling indoor air carbon dioxide concentration using grey-box models

Abstract

Predictive control is the strategy that has the greatest reported benefits when it is implemented in a building energy management system. Predictive control requires low-order models to assess different scenarios and determine which strategy should be implemented to achieve a good compromise between comfort, energy consumption and energy cost. Usually, a deterministic approach is used to create low-order models to estimate the indoor CO2 concentration using the differential equation of the tracer-gas mass balance. However, the use of stochastic differential equations based on the tracer-gas mass balance is not common. The objective of this paper is to assess the potential of creating predictive models for a specific room using for the first time a stochastic grey-box modelling approach to estimate future CO2 concentrations. First of all, a set of stochastic differential equations are defined. Then, the model parameters are estimated using a maximum likelihood method. Different models are defined, and tested using a set of statistical methods. The approach used combines physical knowledge and information embedded in the monitored data to identify a suitable parametrization for a simple model that is more accurate than commonly used deterministic approaches. As a consequence, predictive control can be easily implemented in energy management systems.Peer ReviewedPostprint (author's final draft

Personal CO2 bubble: Context-dependent variations and wearable sensors usability

“…Given the looseness of the terms microenvironment and inhalation zone CO2 (i.e. entire building types have been used as a microenvironment, and in current IAQ practices exposure almost never includes the self-exposure that we explored here) [22,23], the 'background' signal we derive from the wearable sensor is more representative of actual inhalation zone CO2 than the background signal taken from a wall mounted sensor. Though wearable sensors do not offer a viable method for obtaining personal inhalation zone CO2 measurement, it can potentially be integrated into pre-existing systems to contribute to better indoor air quality [24].…”

Comparison of model identification techniques for MPC in all-air HVAC systems in an educational building

“…Instead the motion signal of 1 or 0 is used to multiply with the CO2 generation rate to calculate the room CO2 generation. In a similar study [15] a RMSE was found of 41 ppm during a 4 day period for a 1 step ahead prediction with a 15 minute time interval. In this study a value is found of 46 ppm for the RC counting and 41 ppm for the ARX counting scenario.…”

“…±50 ppm). Macarulla et al [15] used a stochastic grey box model to predict CO2 concentrations inside an office. To predict CO2 concentrations, different grey box models were developed with increased complexity for predicting the CO2 concentration.…”

Estimation of a room ventilation air change rate using a stochastic grey-box modelling approach

“…The drift term is a function that describes the well-known physics of the system. In this study, the tracer-gas mass balance [15][16][17][18][19] was used as a drift term 10 (Eq. 3), representing the change in CO 2 concentration (C int ) at a point in time in a room with volume V r and two ventilation flows and :…”

“…Hence, walls, ceiling and furniture do not absorb CO 2 . Finally, the above equation assumes a perfectly mixed condition, and constant ventilation air flows [19]. In this research, the two ventilation air flows that were considered were constant natural air flows.…”