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Modelling indoor air carbon dioxide concentration using grey-box models
dc.contributor.author | Macarulla Martí, Marcel |
dc.contributor.author | Casals Casanova, Miquel |
dc.contributor.author | Carnevali, Matteo |
dc.contributor.author | Forcada Matheu, Núria |
dc.contributor.author | Gangolells Solanellas, Marta |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Projectes i de la Construcció |
dc.date.accessioned | 2017-03-06T15:12:13Z |
dc.date.available | 2019-03-01T01:30:16Z |
dc.date.issued | 2017-05 |
dc.identifier.citation | 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. |
dc.identifier.issn | 0360-1323 |
dc.identifier.uri | http://hdl.handle.net/2117/101974 |
dc.description.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. |
dc.format.extent | 9 p. |
dc.language.iso | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Edificació::Instal·lacions i acondicionament d'edificis::Instal·lacions de ventilació |
dc.subject.lcsh | Predictive control |
dc.subject.lcsh | Air--Pollution |
dc.subject.lcsh | Indoor air pollution |
dc.subject.lcsh | Buildings--Environmental engineering |
dc.subject.other | Indoor air quality |
dc.subject.other | Ventilation |
dc.subject.other | Simulation |
dc.subject.other | Stochastic methods |
dc.subject.other | CO2 prediction |
dc.subject.other | Low-order model |
dc.title | Modelling indoor air carbon dioxide concentration using grey-box models |
dc.type | Article |
dc.subject.lemac | Control predictiu |
dc.subject.lemac | Aire -- Contaminació |
dc.subject.lemac | Contaminació de l'ambient interior |
dc.subject.lemac | Edificis -- Enginyeria ambiental |
dc.contributor.group | Universitat Politècnica de Catalunya. GRIC - Grup de Recerca i Innovació de la Construcció |
dc.identifier.doi | 10.1016/j.buildenv.2017.02.022 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0360132317300823 |
dc.rights.access | Open Access |
local.identifier.drac | 19747518 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Macarulla, M.; Casals, M.; Carnevali, M.; Forcada, N.; Gangolells, M. |
local.citation.publicationName | Building and environment |
local.citation.volume | 117 |
local.citation.startingPage | 146 |
local.citation.endingPage | 153 |
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