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Deterministic Mathematical Model of Energy Demand of Single-Family Building with Different Parameters and Orientation of Windows in Climatic Conditions of Poland

Author

Listed:
  • Walery Jezierski

    (Department of Sustainable Construction and Building Systems, Faculty of Civil Engineering and Environmental Sciences, Bialystok University of Technology, 15-351 Bialystok, Poland)

  • Adam Święcicki

    (Department of Sustainable Construction and Building Systems, Faculty of Civil Engineering and Environmental Sciences, Bialystok University of Technology, 15-351 Bialystok, Poland)

  • Anna Justyna Werner-Juszczuk

    (Department of Sustainable Construction and Building Systems, Faculty of Civil Engineering and Environmental Sciences, Bialystok University of Technology, 15-351 Bialystok, Poland)

Abstract

Location is crucial when it comes to reducing the energy demand of buildings. Deterministic mathematical models of the energy demand of a single-family building were developed for the cities of Wrocław and Suwałki, representing the mild and severe climatic conditions of Poland, respectively, and compared with energy demand for Białystok, representing medium conditions. Models include the windows area, heat transfer coefficient, solar radiation transmittance of glazing, and orientation of windows. For medium conditions (Białystok), the energy demand is 18.3% higher than for mild conditions (Wrocław) and 7.3% lower than for severe climate conditions (Suwałki). Location does not influence the nature of the effect of the factors on energy demand, which increases with an increase in heat transfer coefficient and a decrease in window area, glazing solar radiation transmittance, and orientation change from north to south. The large impact of solar gains was proved. The optimisation procedure was performed and mathematical descriptions of recommended parameters were created to ensure the equivalent energy efficiency of windows for each orientation and location. For Bialystok, north-facing windows can have an area 1.32 times larger and south-facing windows 1.48 times smaller than east-facing windows to ensure a building’s energy demand remains constant.

Suggested Citation

  • Walery Jezierski & Adam Święcicki & Anna Justyna Werner-Juszczuk, 2024. "Deterministic Mathematical Model of Energy Demand of Single-Family Building with Different Parameters and Orientation of Windows in Climatic Conditions of Poland," Energies, MDPI, vol. 17(10), pages 1-19, May.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2360-:d:1393993
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    References listed on IDEAS

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    1. Lee, Soo-Jin & Song, Seung-Yeong, 2022. "Time-series analysis of the effects of building and household features on residential end-use energy," Applied Energy, Elsevier, vol. 312(C).
    2. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    3. Yan, Ran & Ma, Minda & Zhou, Nan & Feng, Wei & Xiang, Xiwang & Mao, Chao, 2023. "Towards COP27: Decarbonization patterns of residential building in China and India," Applied Energy, Elsevier, vol. 352(C).
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