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Relationship Analysis and Optimisation of Space Layout to Improve the Energy Performance of Office Buildings

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  • Tiantian Du

    (China National Engineering Research Center for Human Settlement, China Architecture Design and Research Group, Beijing 100044, China
    School of Architecture, Tsinghua University, Beijing 100084, China
    Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BZ Delft, The Netherlands)

  • Michela Turrin

    (Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BZ Delft, The Netherlands)

  • Sabine Jansen

    (Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BZ Delft, The Netherlands)

  • Andy van den Dobbelsteen

    (Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BZ Delft, The Netherlands)

  • Francesco De Luca

    (Academy of Architecture and Urban Studies, Department of Civil Engineering and Architecture, Tallinn University of Technology, 19086 Tallinn, Estonia)

Abstract

Architectural space layout has proven to be influential on building energy performance. However, the relationship between different space layouts and their consequent energy demands has not yet been systematically studied. This study thoroughly investigates such a relationship. In order to do so, a computational method was developed, which includes a method to generate space layouts featuring energy-related variables and an assessment method for energy demand. Additionally, a design of experiments was performed, and its results were used to analyse the relationship between space layouts and energy demands. In order to identify their relationship, four types of design indicators of space layout were proposed, both for the overall layout and for each function. Finally, several optimisations were performed to minimise heating, cooling and lighting demands. The optimisation results showed that the maximum reduction between different layouts was up to 54% for lighting demand, 51% for heating demand and 38% for cooling demand. The relationship analysis shows that when comparing the four types of design indicators, the façade area-to-floor area ratio showed a stronger correlation with energy demands than the façade area ratio, floor area ratio and height-to-depth ratio. Overall, this study shows that designing a space layout helps to reduce energy demands for heating, cooling and lighting, and also provides a reference for other researchers and designers to optimise space layout with improved energy performance.

Suggested Citation

  • Tiantian Du & Michela Turrin & Sabine Jansen & Andy van den Dobbelsteen & Francesco De Luca, 2022. "Relationship Analysis and Optimisation of Space Layout to Improve the Energy Performance of Office Buildings," Energies, MDPI, vol. 15(4), pages 1-26, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1268-:d:745470
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    References listed on IDEAS

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    1. Evins, Ralph, 2013. "A review of computational optimisation methods applied to sustainable building design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 230-245.
    2. Ramos, Greici & Ghisi, Enedir, 2010. "Analysis of daylight calculated using the EnergyPlus programme," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1948-1958, September.
    3. Zhang, Shufan & Ma, Minda & Li, Kai & Ma, Zhili & Feng, Wei & Cai, Weiguang, 2022. "Historical carbon abatement in the commercial building operation: China versus the US," Energy Economics, Elsevier, vol. 105(C).
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