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Handling uncertainty in housing stock models

机译：处理住房存量模型中的不确定性

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摘要

Housing stock models can be useful tools in helping to assess the environmental and socio-economic impacts of retrofits to residential buildings; however, existing housing stock models are not able to quantify the uncertainties that arise in the modelling process from various sources, thus limiting the role that they can play in helping decision makers. This paper examines the different sources of uncertainty involved in housing stock models and proposes a framework for handling these uncertainties. This framework involves integrating probabilistic sensitivity analysis with a Bayesian calibration process in order to quantify uncertain parameters more accurately. The proposed framework is tested on a case study building, and suggestions are made on how to expand the framework for retrofit analysis at an urban-scale.
机译：住房存量模型可以作为有用的工具，有助于评估住宅建筑改建对环境和社会经济的影响；但是，现有的住房存量模型无法量化来自各种来源的建模过程中出现的不确定性，因此限制了它们在帮助决策者中的作用。本文研究了住房存量模型涉及的各种不确定性来源，并提出了处理这些不确定性的框架。该框架涉及将概率敏感性分析与贝叶斯校准过程集成在一起，以便更准确地量化不确定参数。在一个案例研究大楼上对提出的框架进行了测试，并就如何扩展城市规模的改造分析框架提出了建议。

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  • 来源
    《Building and Environment》 |2012年第2期|p.35-47|共13页
  • 作者

    A.T. Booth; R. Choudhary; D.J. Spiegelhalter;

  • 作者单位

    Energy Efficient Cities Initiative, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK;

    Energy Efficient Cities Initiative, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK;

    Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 OWB, UK;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    uncertainty; energy; housing; stock; bayesian; calibration;

    机译：不确定;能源;住房;股票;贝叶斯校准;

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