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Using Regression Model to Develop Green Building Energy Simulation by BIM Tools

Author

Listed:
  • Faham Tahmasebinia

    (School of Civil Engineering, The University of Sydney, Sydney, NSW 2006, Australia)

  • Ruifeng Jiang

    (School of Civil Engineering, The University of Sydney, Sydney, NSW 2006, Australia)

  • Samad Sepasgozar

    (School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia)

  • Jinlin Wei

    (School of Civil Engineering, The University of Sydney, Sydney, NSW 2006, Australia)

  • Yilin Ding

    (School of Civil Engineering, The University of Sydney, Sydney, NSW 2006, Australia)

  • Hongyi Ma

    (School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia)

Abstract

Energy consumption in the building sector poses a huge burden in terms of global energy and pollution. Recent advancements in building information modelling and simulating building energy performance (BEP) have provided opportunities for energy optimization. The use of building information modelling (BIM) also has increased significantly in the last decade based on the requirement to accommodate and manage data in buildings. By using the data, some building information modelling tools have developed the function of energy analysis. This paper aims to identify design parameters critical to BEP to assist architects in the initial stages of building design and to investigate their relationship. The outcomes of the prototype model’s energy simulations were then used to construct multilinear regression models. For the rest of the independent building design variables, linear regression models are used to analyse the relationship between it and energy consumption. It was concluded that, in the same building conditions, diamond-shaped buildings have the highest energy consumption, while triangle-shaped buildings showed the most efficient energy performance through energy simulations for seven fundamental prototype building models based on Autodesk Kits, Green Building Studio (GBS) with a Doe-2 engine. In addition, the developed regression models are validated to within 10% error via a case study of the ABS building. At the end of this paper, recommendations are provided on energy optimisation for the initial stages of building design. The parametric analysis of design variables in this study contributed to the total energy consumption at the early phases of design and recommendations on energy optimization.

Suggested Citation

  • Faham Tahmasebinia & Ruifeng Jiang & Samad Sepasgozar & Jinlin Wei & Yilin Ding & Hongyi Ma, 2022. "Using Regression Model to Develop Green Building Energy Simulation by BIM Tools," Sustainability, MDPI, vol. 14(10), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6262-:d:820362
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    References listed on IDEAS

    as
    1. Gao, Hao & Koch, Christian & Wu, Yupeng, 2019. "Building information modelling based building energy modelling: A review," Applied Energy, Elsevier, vol. 238(C), pages 320-343.
    2. Shabir Hussain Khahro & Danish Kumar & Fida Hussain Siddiqui & Tauha Hussain Ali & Muhammad Saleem Raza & Ali Raza Khoso, 2021. "Optimizing Energy Use, Cost and Carbon Emission through Building Information Modelling and a Sustainability Approach: A Case-Study of a Hospital Building," Sustainability, MDPI, vol. 13(7), pages 1-18, March.
    3. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    4. Mohammad K. Najjar & Vivian W. Y. Tam & Leandro Torres Di Gregorio & Ana Catarina Jorge Evangelista & Ahmed W. A. Hammad & Assed Haddad, 2019. "Integrating Parametric Analysis with Building Information Modeling to Improve Energy Performance of Construction Projects," Energies, MDPI, vol. 12(8), pages 1-22, April.
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    Cited by:

    1. Haotian Zheng & Shuchuan Zhang & Junqi Zhu & Ziyan Zhu & Xin Fang, 2022. "Evacuation in Buildings Based on BIM: Taking a Fire in a University Library as an Example," IJERPH, MDPI, vol. 19(23), pages 1-21, December.
    2. Jessie Bravo & Roger Alarcón & Carlos Valdivia & Oscar Serquén, 2023. "Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru," Sustainability, MDPI, vol. 15(11), pages 1-25, June.

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