Modelling indoor air carbon dioxide concentration using grey-box models

• Published in: Building and environment 2017-05, Vol.117, p.146-153
• Main Authors: Macarulla, Marcel, Casals, Miquel, Carnevali, Matteo, Forcada, Núria, Gangolells, Marta
• Format: Article
• Language: English
• Subjects: Air; Aire; Buildings; Carbon dioxide; Carbon dioxide concentration; CO2 prediction; Comfort; Contaminació; Contaminació de l'ambient interior; Control predictiu; Control systems; Differential equations; Edificació; Edificis; Energy consumption; Energy management; Energy management systems; Enginyeria ambiental; Environmental engineering; Indoor air pollution; Indoor air quality; Indoor environments; Instal·lacions de ventilació; Instal·lacions i acondicionament d'edificis; Low-order model; Mathematical models; Maximum likelihood estimates; Modelling; Parameter estimation; Parameterization; Pollution; Prediction models; Predictive control; Probability theory; Randomness; Simulation; Statistical analysis; Statistical methods; Stochastic methods; Ventilation; Àrees temàtiques de la UPC
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2017.02.022

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. •Grey-box modelling is used to model the indoor CO2 concentration.•Stochastic approach enables to identify suitable parametrization.•The proposed approach was found more accurate than currently used deterministic approaches.•The optimal RC-network to model indoor CO2 concentration in a room is identified.