2026-04-17T19:20:37Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1019742026-01-21T04:56:44Zcom_2072_1033col_2072_452950

Modelling indoor air carbon dioxide concentration using grey-box models Macarulla Martí, Marcel Casals Casanova, Miquel Carnevali, Matteo Forcada Matheu, Núria Gangolells Solanellas, Marta Universitat Politècnica de Catalunya. Departament d'Enginyeria de Projectes i de la Construcció Universitat Politècnica de Catalunya. GRIC - Grup de Recerca i Innovació de la Construcció Àrees temàtiques de la UPC::Edificació::Instal·lacions i acondicionament d'edificis::Instal·lacions de ventilació Predictive control Air--Pollution Indoor air pollution Buildings--Environmental engineering Indoor air quality Ventilation Simulation Stochastic methods CO2 prediction Low-order model Control predictiu Aire -- Contaminació Contaminació de l'ambient interior Edificis -- Enginyeria ambiental 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 Reviewed Postprint (author's final draft) 2017-05 Article Macarulla, M., Casals, M., Carnevali, M., Forcada, N., Gangolells, M. Modelling indoor air carbon dioxide concentration using grey-box models. "Building and environment", Maig 2017, vol. 117, p. 146-153. 0360-1323 https://hdl.handle.net/2117/101974 10.1016/j.buildenv.2017.02.022 eng http://www.sciencedirect.com/science/article/pii/S0360132317300823 http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access 9 p. application/pdf