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Monte Carlo vs. Fuzzy Monte Carlo Simulation for Uncertainty and Global Sensitivity Analysis

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  • Young-Jin Kim

    (Division of Architecture, Architectural Engineering and Civil Engineering, Sunmoon University, Asan, Chungnam 336-708, Korea)

Abstract

Monte Carlo simulation (MCS) has been widely used for the uncertainty propagations of building simulation tools. In general, most unknown inputs for the MCS are regarded as single probability distributions based on experts’ subjective judgements and assumptions, when simulation information and measured data are inaccurate and insufficient. However, this can lead to meaningless and untrustworthy results, since the results are obtained using only single probability distributions without considering reducible possibilities of some unknown inputs. This paper introduces a fuzzy MCS for dealing with the aforementioned problems. In comparison with the MCS, the fuzzy MCS has the advantage of considering the aleatory and epistemic uncertainty, and can provide a family of probability distributions. This paper also discusses how fuzzy MCS could be effectively used for uncertainty and global sensitivity analysis.

Suggested Citation

  • Young-Jin Kim, 2017. "Monte Carlo vs. Fuzzy Monte Carlo Simulation for Uncertainty and Global Sensitivity Analysis," Sustainability, MDPI, vol. 9(4), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:539-:d:94685
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    References listed on IDEAS

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    1. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
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    Cited by:

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    2. Molin Sun & Zhongyi Zheng & Longhui Gang, 2018. "Uncertainty Analysis of the Estimated Risk in Formal Safety Assessment," Sustainability, MDPI, vol. 10(2), pages 1-16, January.
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    4. Xin Yang & Yifei Sima & Yabo Lv & Mingwei Li, 2023. "Research on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak: A Case of Henan Province in China," Sustainability, MDPI, vol. 15(13), pages 1-18, June.
    5. Prabatha, Tharindu & Karunathilake, Hirushie & Mohammadpour Shotorbani, Amin & Sadiq, Rehan & Hewage, Kasun, 2021. "Community-level decentralized energy system planning under uncertainty: A comparison of mathematical models for strategy development," Applied Energy, Elsevier, vol. 283(C).

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