A hierarchical Bayesian framework for calibrating micro-level models with macro-level data

Owners of housing stocks require reliable and flexible tools to assess the impact of retrofits technologies. Bottom-up engineering-based housing stock models can help to serve such a function. These models require calibrating, using micro-level energy measurements at the building level, to improve m...

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Bibliographic Details
Published in:Journal of building performance simulation 2013-07, Vol.6 (4), p.293-318
Main Authors: Booth, A.T., Choudhary, R., Spiegelhalter, D.J.
Format: Article
Language:English
Subjects:
Bayesian
Bayesian analysis
Calibration
Energy use
Estimates
Housing
housing stock
Raw materials
Regression
retrofit
Studies
uncertainty
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Summary:Owners of housing stocks require reliable and flexible tools to assess the impact of retrofits technologies. Bottom-up engineering-based housing stock models can help to serve such a function. These models require calibrating, using micro-level energy measurements at the building level, to improve model accuracy; however, the only publicly available data for the UK housing stock is at the macro-level, at the district, urban, or national scale. This paper outlines a method for using macro-level data to calibrate micro-level models. A hierarchical framework is proposed, utilizing a combination of regression analysis and Bayesian inference. The result is a Bayesian regression method that generates estimates of the average energy use for different dwelling types whilst quantifying uncertainty in both the empirical data and the generated energy estimates. Finally, the Bayesian regression method is validated and the use of the hierarchical Bayesian calibration framework is demonstrated.
ISSN:1940-1493
1940-1507
DOI:10.1080/19401493.2012.723750