DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2021-02-05 - Article/Dans un journal avec peer-review - Anglais - 20 page(s)

Bagheri Ali, Genikomsakis Konstantinos, Feldheim Véronique , Ioakimidis Christos S, "Sensitivity Analysis of 4R3C Model Parameters with Respect to Structure and Geometric Characteristics of Buildings" in Energies, 14, 657, https://doi.org/10.3390/en14030657

  • Edition : Multidisciplinary Digital Publishing Institute (MDPI) (Switzerland)
  • Codes CREF : Recherche énergétique (DI2290), Transfert de chaleur (DI2211)
  • Unités de recherche UMONS : Thermique et Combustion (F704)
  • Instituts UMONS : Institut de Recherche en Energétique (Energie)
Texte intégral :

Abstract(s) :

(Anglais) Data-driven models, either simplified or detailed, have been extensively used in the literature for energy assessment in buildings and districts. However, the uncertainty of the estimated parameters, especially of thermal masses in resistance–capacitance (RC) models, still remains a significant challenge, given the wide variety of buildings functionalities, typologies, structures and geometries. Therefore, the sensitivity analysis of the estimated parameters in RC models with respect to different geometric characteristics is necessary to examine the accuracy of identified models. In this work, heavy- and light-structured buildings are simulated in Transient System Simulation Tool (TRNSYS) to analyze the effects of four main geometric characteristics on the total heat demand, maximum heat power and the estimated parameters of an RC model (4R3C), namely net-floor area, windows-to-floor ratio, aspect ratio, and orientation angle. Executing more than 700 simulations in TRNSYS and comparing the outcomes with their corresponding 4R3C model shows that the thermal resistances of 4-facade building structures are estimated with good accuracy regardless of their geometric features, while the insulation level has the highest impact on the estimated parameters. Importantly, the results obtained also indicate that the 4R3C model can estimate the indoor temperature with a mean square error of less than 0.5 ◦C for all cases.