Sensitivity Analysis of 4R3C Model Parameters with Respect to Structure and Geometric Characteristics of Buildings

Ali Bagheri, Konstantinos N. Genikomsakis, Véronique Feldheim and Christos S. Ioakimidis
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Ali Bagheri: European Research Area Chair ‘Net-Zero Energy Efficiency on City Districts, NZED’ Unit, Research Institute for Energy, University of Mons, Rue de l’Epargne 56, 7000 Mons, Belgium
Konstantinos N. Genikomsakis: Inteligg P.C., Karaiskaki 28, 10554 Athens, Greece
Véronique Feldheim: Thermal Engineering and Combustion Unit, Rue de l’Epargne 56, University of Mons, 7000 Mons, Belgium
Christos S. Ioakimidis: Center for Research and Technology Hellas/Hellenic Institute of Transport, CERTH/HIT), 6th Km Charilaou—Thermi Rd., Thermi, Thessaloniki, Macedonia, 57001 Hellas, Greece

Energies, 2021, vol. 14, issue 3, 1-20

Abstract: 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.

Keywords: building energy performance; building geometry; building structure; RC models; sensitivity analysis; system identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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