Hybrid grey box modelling of indoor air quality and thermal dynamics in indoor environments
The primary objective of this paper is to develop a hybrid grey box model that integrates air and thermal dynamics to improve accuracy in both domains. The methodology involved developing four grey box models to estimate ventilation airflows using indoor CO2 concentration data and six thermal models...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/427490 |
| Acceso en línea: | https://hdl.handle.net/2117/427490 https://dx.doi.org/10.1016/j.enbuild.2025.115528 |
| Access Level: | acceso abierto |
| Palabra clave: | Grey box modelling IAQ Thermal dynamics Ventilation systems Parameter identification Àrees temàtiques de la UPC::Edificació::Instal·lacions i acondicionament d'edificis Àrees temàtiques de la UPC::Física::Termodinàmica |
| Sumario: | The primary objective of this paper is to develop a hybrid grey box model that integrates air and thermal dynamics to improve accuracy in both domains. The methodology involved developing four grey box models to estimate ventilation airflows using indoor CO2 concentration data and six thermal models to estimate thermal properties and heat gains using indoor temperature data. To ensure accurate parameterization, measurements of outdoor conditions, occupancy, and HVAC operations were incorporated. The results revealed that models treating infiltration and mechanical ventilation as mutually exclusive (IAQ-3 and IAQ-4) and those integrating ventilation heat gains from estimated airflows (T-6) performed most effectively. This hybrid approach underscores the benefits of incorporating ventilation heat loos or gains, based on airflow estimation derived from indoor air quality (IAQ) models, into thermal modelling, significantly improving accuracy and reducing parameter variability. The findings demonstrate the potential of this methodology for applications in ventilation management and HVAC optimization. By enhancing energy efficiency and improving indoor air quality, this approach supports the development of healthier, more sustainable indoor environments. |
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