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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ali Bagheri & Konstantinos N. Genikomsakis & Véronique Feldheim & Christos S. Ioakimidis, 2021. "Sensitivity Analysis of 4R3C Model Parameters with Respect to Structure and Geometric Characteristics of Buildings," Energies, MDPI, vol. 14(3), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:657-:d:488515
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    References listed on IDEAS

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    1. De Jaeger, Ina & Reynders, Glenn & Ma, Yixiao & Saelens, Dirk, 2018. "Impact of building geometry description within district energy simulations," Energy, Elsevier, vol. 158(C), pages 1060-1069.
    2. Schweiger, Gerald & Heimrath, Richard & Falay, Basak & O'Donovan, Keith & Nageler, Peter & Pertschy, Reinhard & Engel, Georg & Streicher, Wolfgang & Leusbrock, Ingo, 2018. "District energy systems: Modelling paradigms and general-purpose tools," Energy, Elsevier, vol. 164(C), pages 1326-1340.
    3. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2020. "Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls," Energies, MDPI, vol. 13(12), pages 1-18, June.
    4. Mattia De Rosa & Marcus Brennenstuhl & Carlos Andrade Cabrera & Ursula Eicker & Donal P. Finn, 2019. "An Iterative Methodology for Model Complexity Reduction in Residential Building Simulation," Energies, MDPI, vol. 12(12), pages 1-20, June.
    5. Ali Bagheri & Véronique Feldheim & Christos S. Ioakimidis, 2018. "On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings," Energies, MDPI, vol. 11(4), pages 1-20, April.
    6. Iturriaga, E. & Aldasoro, U. & Terés-Zubiaga, J. & Campos-Celador, A., 2018. "Optimal renovation of buildings towards the nearly Zero Energy Building standard," Energy, Elsevier, vol. 160(C), pages 1101-1114.
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    Cited by:

    1. Filip Belić & Dražen Slišković & Željko Hocenski, 2021. "Detailed Thermodynamic Modeling of Multi-Zone Buildings with Resistive-Capacitive Method," Energies, MDPI, vol. 14(21), pages 1-24, October.
    2. Piotr Michalak, 2023. "Simulation and Experimental Study on the Use of Ventilation Air for Space Heating of a Room in a Low-Energy Building," Energies, MDPI, vol. 16(8), pages 1-17, April.

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