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Influence of Building Envelope Modeling Parameters on Energy Simulation Results

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  • Simon Muhič

    (Institute for Renewable Energy and Efficient Exergy Use, INOVEKS d.o.o., Cesta 2. grupe odredov 17, 1295 Ivančna Gorica, Slovenia
    Faculty of Industrial Engineering Novo Mesto, Šegova ulica 112, 8000 Novo Mesto, Slovenia
    Rudolfovo—Science and Technology Centre Novo Mesto, Podbreznik 15, 8000 Novo Mesto, Slovenia)

  • Dimitrije Manić

    (The Innovation Center of the Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, 11120 Belgrade, Serbia)

  • Ante Čikić

    (Department of Mechatronics, University North, 104. Brigade 3, 42000 Varaždin, Croatia)

  • Mirko Komatina

    (Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, 11120 Belgrade, Serbia)

Abstract

This study investigates the influence of input values for building energy model parameters on simulation results, with the aim of improving the reliability and sustainability of energy performance assessments. Dynamic simulations were conducted in TRNSYS for three theoretical multi-residential buildings, varying parameters such as referent model dimensions, infiltration rates, envelope thermophysical properties, and interior thermal capacitance. The case study, based in Slovenia, demonstrates that glazing-related parameters, particularly the solar heat gain coefficient (g-value), exert the most significant influence—reducing the g-value from 0.62 to 0.22 decreased simulated heating ( q H,nd ) and cooling ( q C,nd ) demands by 25% and 95%, respectively. In contrast, referent dimensions for modeled floor area proved least influential. For Building III (BSF = 0.36), dimensional variations altered results by less than ±1%, whereas, for Building I (BSF = 0.62), variations reached up to ±20%. In general, lower shape factors yield more robust energy models that are less sensitive to input deviations. These findings are critical for promoting resource-efficient simulation practices and ensuring that energy modeling contributes effectively to sustainable building design. Understanding which inputs warrant detailed attention supports more targeted and meaningful simulation workflows, enabling more accurate and impactful strategies for building energy efficiency and long-term environmental performance.

Suggested Citation

  • Simon Muhič & Dimitrije Manić & Ante Čikić & Mirko Komatina, 2025. "Influence of Building Envelope Modeling Parameters on Energy Simulation Results," Sustainability, MDPI, vol. 17(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5276-:d:1674019
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    References listed on IDEAS

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    1. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    2. Oldewurtel, Frauke & Sturzenegger, David & Morari, Manfred, 2013. "Importance of occupancy information for building climate control," Applied Energy, Elsevier, vol. 101(C), pages 521-532.
    3. Johra, Hicham & Heiselberg, Per, 2017. "Influence of internal thermal mass on the indoor thermal dynamics and integration of phase change materials in furniture for building energy storage: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 19-32.
    4. Zakula, Tea & Bagaric, Marina & Ferdelji, Nenad & Milovanovic, Bojan & Mudrinic, Sasa & Ritosa, Katia, 2019. "Comparison of dynamic simulations and the ISO 52016 standard for the assessment of building energy performance," Applied Energy, Elsevier, vol. 254(C).
    5. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    6. Al-Sanea, Sami A. & Zedan, M.F., 2012. "Effect of thermal bridges on transmission loads and thermal resistance of building walls under dynamic conditions," Applied Energy, Elsevier, vol. 98(C), pages 584-593.
    7. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
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