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A platform for research: civil engineering, architecture and urbanism
Modelling indoor air carbon dioxide concentration using grey-box models
https://orcid.org/0000-0002-5469-7291
AbstractPredictive 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.
HighlightsGrey-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.
Modelling indoor air carbon dioxide concentration using grey-box models
https://orcid.org/0000-0002-5469-7291
AbstractPredictive 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.
HighlightsGrey-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.
Modelling indoor air carbon dioxide concentration using grey-box models
https://orcid.org/0000-0002-5469-7291
AbstractPredictive 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.
HighlightsGrey-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.
Modelling indoor air carbon dioxide concentration using grey-box models
Macarulla, Marcel (author) / Casals, Miquel (author) / Carnevali, Matteo (author) / Forcada, Núria (author) / Gangolells, Marta (author)
Building and Environment ; 117 ; 146-153
2017-02-25
8 pages
Article (Journal)
Electronic Resource
English
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