Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Modelling indoor air carbon dioxide concentration using grey-box models
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)
Modelling indoor air carbon dioxide concentration using grey-box models
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)
Modelling indoor air carbon dioxide concentration using grey-box models
Macarulla Martí, Marcel (Autor:in) / Casals Casanova, Miquel (Autor:in) / Carnevali, Matteo (Autor:in) / Forcada Matheu, Núria (Autor:in) / Gangolells Solanellas, Marta (Autor:in) / 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
01.05.2017
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Contaminació de l'ambient interior , Control predictiu , Edificis -- Enginyeria ambiental , Indoor air quality , Ventilation , Air--Pollution , Buildings--Environmental engineering , Àrees temàtiques de la UPC::Edificació::Instal·lacions i acondicionament d'edificis::Instal·lacions de ventilació , Stochastic methods , CO2 prediction , Simulation , Predictive control , Aire -- Contaminació , Indoor air pollution , Low-order model
DDC:
690
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