Modelling indoor air carbon dioxide concentration using grey-box models. (15th May 2017) - Macarulla, Marcel; Casals, Miquel; Carnevali, Matteo; Forcada, Núria; Gangolells, Marta
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. Highlights: 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.