Calibrating micro-level models with macro-level data using bayesian regression analysis. Booth, A., Choudhary, R., & Initiative, E. E. C. In Proceedings of the 12th IBPSA Conference, November 14–16, pages 641–648. 2012.
abstract   bibtex   
Bottom-up engineering-based housing stock models play a useful role in assessing the impact of retrofits for residential buildings. Such models require calibrating, using micro-level energy measurements, to improve model accuracy; however, the only publicly available data for the UK housing stock is at the macro-level. This paper outlines a method for using macro-level data to calibrate micro-level models. A combination of regression analysis and Bayesian inference is pro- posed. The result is a Bayesian regression method that generates estimates of the average energy use for different dwelling types, whilst quantifying uncertainty in the empirical energy data and the generated energy estimates.
@incollection{booth2012calibrating,
  title={Calibrating micro-level models with macro-level data using bayesian regression analysis},
  author={Booth, Adam and Choudhary, Ruchi and Initiative, Energy Efficient Cities},
  booktitle={Proceedings of the 12th IBPSA Conference, November 14--16},
  pages={641--648},
  year={2012},
  abstract={ Bottom-up engineering-based housing stock models play a useful role in assessing the impact of retrofits for residential buildings. Such models require calibrating, using micro-level energy measurements, to improve model accuracy; however, the only publicly available data for the UK housing stock is at the macro-level. This paper outlines a method for using macro-level data to calibrate micro-level models. A combination of regression analysis and Bayesian inference is pro- posed. The result is a Bayesian regression method that generates estimates of the average energy use for different dwelling types, whilst quantifying uncertainty in the empirical energy data and the generated energy estimates. }
}

Downloads: 0