Journal
BUILDING AND ENVIRONMENT
Volume 48, Issue -, Pages 35-47Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2011.08.016
Keywords
Uncertainty; Energy; Housing; Stock; Bayesian; Calibration
Funding
- EPSRC [EP/F034350/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/F034350/1] Funding Source: researchfish
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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. (C) 2011 Elsevier Ltd. All rights reserved.
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