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Building simulations supporting decision making in early design – A review

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  • Østergård, Torben
  • Jensen, Rasmus L.
  • Maagaard, Steffen E.

Abstract

The building design community is challenged by continuously increasing energy demands, which are often combined with ambitious goals for indoor environment, for environmental impact, and for building costs. To aid decision-making, building simulation is widely used in the late design stages, but its application is still limited in the early stages in which design decisions have a major impact on final building performance and costs. The early integration of simulation software faces several challenges, which include time-consuming modeling, rapid change of the design, conflicting requirements, input uncertainties, and large design variability. In addition, building design is a multi-collaborator discipline, where design decisions are influenced by architects, engineers, contractors, and building owners. This review covers developments in both academia and in commercial software industry that target these challenges. Identified research areas include statistical methods, optimisation, proactive simulations, knowledge based input generation, and interoperability between CAD-software and building performance software. Based on promising developments in literature, we propose a simulation framework that facilitates proactive, intelligent, and experience based building simulation which aid decision making in early design. To find software candidates accommodating this framework, we compare existing software with regard to intended usage, interoperability, complexity, objectives, and ability to perform various parametric simulations.

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  • Østergård, Torben & Jensen, Rasmus L. & Maagaard, Steffen E., 2016. "Building simulations supporting decision making in early design – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 187-201.
  • Handle: RePEc:eee:rensus:v:61:y:2016:i:c:p:187-201
    DOI: 10.1016/j.rser.2016.03.045
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    10. Ohlsson, K.E. Anders & Olofsson, Thomas, 2021. "Benchmarking the practice of validation and uncertainty analysis of building energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
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    12. Liwei Wen & Kyosuke Hiyama, 2018. "Target Air Change Rate and Natural Ventilation Potential Maps for Assisting with Natural Ventilation Design During Early Design Stage in China," Sustainability, MDPI, vol. 10(5), pages 1-16, May.
    13. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    14. Tian Han & Qiong Huang & Anxiao Zhang & Qi Zhang, 2018. "Simulation-Based Decision Support Tools in the Early Design Stages of a Green Building—A Review," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    15. Chen, Xia & Geyer, Philipp, 2022. "Machine assistance in energy-efficient building design: A predictive framework toward dynamic interaction with human decision-making under uncertainty," Applied Energy, Elsevier, vol. 307(C).
    16. Suzi Dilara Mangan, 2023. "A Performance-Based Decision Support Workflow for Retrofitting Residential Buildings," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    17. 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.
    18. Lee, Junghun & Kim, Jeonggook & Song, Doosam & Kim, Jonghun & Jang, Cheolyong, 2017. "Impact of external insulation and internal thermal density upon energy consumption of buildings in a temperate climate with four distinct seasons," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1081-1088.
    19. Singh, Manav Mahan & Singaravel, Sundaravelpandian & Geyer, Philipp, 2021. "Machine learning for early stage building energy prediction: Increment and enrichment," Applied Energy, Elsevier, vol. 304(C).
    20. Seung Yeoun Choi & Sean Hay Kim, 2022. "Selection of a Transparent Meta-Model Algorithm for Feasibility Analysis Stage of Energy Efficient Building Design: Clustering vs. Tree," Energies, MDPI, vol. 15(18), pages 1-25, September.
    21. Luo, Yongqiang & Zhang, Ling & Bozlar, Michael & Liu, Zhongbing & Guo, Hongshan & Meggers, Forrest, 2019. "Active building envelope systems toward renewable and sustainable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 470-491.
    22. Shaoxiong Li & Le Liu & Changhai Peng, 2020. "A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges," Sustainability, MDPI, vol. 12(4), pages 1-36, February.
    23. Salata, Ferdinando & Ciancio, Virgilio & Dell'Olmo, Jacopo & Golasi, Iacopo & Palusci, Olga & Coppi, Massimo, 2020. "Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms," Applied Energy, Elsevier, vol. 260(C).

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