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Probabilistic prediction of green roof energy performance under parameter uncertainty

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  • Liu, Min (Max)

Abstract

Studies on the quantification of energy benefits of a green roof have so far treated its parameter values only deterministically. In reality, however, these values may scatter over different ranges due to the inherent variation of vegetation and soil properties and also because of the unavoidable deviation from designated values during construction and/or actual operation of a green roof. Under such parameter uncertainty, green roof performance can no longer be predicted deterministically but rather probabilistically. The present study attempts to integrate the whole building energy simulation with a parametric uncertainty analysis. An example office building is used to systematically examine how the cooling and heating energy demands can be reduced by a green roof that replaces a conventional roof, when values of the most significant green roof parameters determined by sensitivity analysis are treated as random variables with prescribed probability distributions. An ensemble of green roof configurations is generated using Monte Carlo simulation with a Latin hypercube sampling technique. The coefficient of variation of the calculated energy savings is found almost linearly related to (with a slope of about 0.4) that of green roof parameters. Finally, implications of probabilistic energy analysis for more reliable green roof design are emphasized.

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  • Liu, Min (Max), 2014. "Probabilistic prediction of green roof energy performance under parameter uncertainty," Energy, Elsevier, vol. 77(C), pages 667-674.
    • Handle: RePEc:eee:energy:v:77:y:2014:i:c:p:667-674
    DOI: 10.1016/j.energy.2014.09.043
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    2. Copiello, Sergio & Gabrielli, Laura & Bonifaci, Pietro, 2017. "Evaluation of energy retrofit in buildings under conditions of uncertainty: The prominence of the discount rate," Energy, Elsevier, vol. 137(C), pages 104-117.
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    4. Patryk Antoszewski & Dariusz Świerk & Michał Krzyżaniak, 2020. "Statistical Review of Quality Parameters of Blue-Green Infrastructure Elements Important in Mitigating the Effect of the Urban Heat Island in the Temperate Climate (C) Zone," IJERPH, MDPI, vol. 17(19), pages 1-36, September.
    5. Mihalakakou, Giouli & Souliotis, Manolis & Papadaki, Maria & Menounou, Penelope & Dimopoulos, Panayotis & Kolokotsa, Dionysia & Paravantis, John A. & Tsangrassoulis, Aris & Panaras, Giorgos & Giannako, 2023. "Green roofs as a nature-based solution for improving urban sustainability: Progress and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).
    6. Li, Hong Xian & Li, Yan & Jiang, Boya & Zhang, Limao & Wu, Xianguo & Lin, Jingyi, 2020. "Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis," Renewable Energy, Elsevier, vol. 149(C), pages 1414-1423.
    7. Ferrante, Patrizia & La Gennusa, Maria & Peri, Giorgia & Rizzo, Gianfranco & Scaccianoce, Gianluca, 2016. "Vegetation growth parameters and leaf temperature: Experimental results from a six plots green roofs' system," Energy, Elsevier, vol. 115(P3), pages 1723-1732.

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