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Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates

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  • Rackes, Adams
  • Melo, Ana Paula
  • Lamberts, Roberto

Abstract

This work develops guidance and tools to understand the performance, improve the design, and simplify the evaluation of naturally ventilated low-rise commercial buildings in warm and hot climates. We conducted ∼50,000 detailed energy and airflow simulations in 427 locations across Brazil, varying 55 parameters representing building morphology, fenestration, construction properties, internal gains, operating times, wind modifiers, flowpaths, window control, and soil traits. Comfort performance was quantified by the average annual fraction of occupied hours that exceeded the upper limit of an adaptive comfort zone, and investigated with sensitivity analysis and machine learning methods. Results indicated that, after climate, building size (both footprint area and number of stories) and internal gains were most influential and were positively associated with discomfort. Adding air movement with ceiling fans and providing for night ventilation both proved highly effective comfort interventions. Except for roof solar absorptance, opaque envelope changes, including increasing insulation or thermal mass, had only marginal impacts. A support vector regression metamodel, requiring 29 easily obtainable inputs plus a weather file, was fit to the simulation results and successfully validated (R2=0.97). The metamodel was developed as a simplified compliance path for naturally ventilated buildings to enhance Brazil’s commercial building performance labeling program, which, because it currently provides such a path only for air conditioned buildings, may discouraging decision-makers from considering even more efficient passive solutions. We use a case study to show how the metamodel, which we will distribute publicly, can also serve as a design tool, and demonstrate that modifying a small set of parameters can drastically improve thermal performance and achieve sustainable comfort in hot and warm climates.

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  • Rackes, Adams & Melo, Ana Paula & Lamberts, Roberto, 2016. "Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates," Applied Energy, Elsevier, vol. 174(C), pages 256-274.
    • Handle: RePEc:eee:appene:v:174:y:2016:i:c:p:256-274
    DOI: 10.1016/j.apenergy.2016.04.081
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    5. Zhang, Ran & Xu, Xiaodong & Liu, Ke & Kong, Lingyu & Wang, Wei & Wortmann, Thomas, 2024. "Airflow modelling for building design: A designers' review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
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    8. Germán Campos Gordillo & Germán Ramos Ruiz & Yves Stauffer & Stephan Dasen & Carlos Fernández Bandera, 2020. "EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB ® and C#," Sustainability, MDPI, vol. 12(2), pages 1-14, January.
    9. Aviv, Dorit & Chen, Kian Wee & Teitelbaum, Eric & Sheppard, Denon & Pantelic, Jovan & Rysanek, Adam & Meggers, Forrest, 2021. "A fresh (air) look at ventilation for COVID-19: Estimating the global energy savings potential of coupling natural ventilation with novel radiant cooling strategies," Applied Energy, Elsevier, vol. 292(C).
    10. Cui, X. & Mohan, B. & Islam, M.R. & Chou, S.K. & Chua, K.J., 2017. "Energy performance evaluation and application of an air treatment system for conditioning building spaces in tropics," Applied Energy, Elsevier, vol. 204(C), pages 1500-1512.
    11. Lu, Mengxue & Lai, Joseph, 2020. "Review on carbon emissions of commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    12. Chen, Xi & Yang, Hongxing, 2018. "Integrated energy performance optimization of a passively designed high-rise residential building in different climatic zones of China," Applied Energy, Elsevier, vol. 215(C), pages 145-158.
    13. 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.
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    16. Westermann, Paul & Welzel, Matthias & Evins, Ralph, 2020. "Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones," Applied Energy, Elsevier, vol. 278(C).

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