Evaluation of calibration efficacy under different levels of uncertainty

Yeonsook Heo, Diane J. Graziano, Leah Guzowski, Ralph T. Muehleisen

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.

Original languageEnglish
Pages (from-to)135-144
Number of pages10
JournalJournal of Building Performance Simulation
Issue number3
Publication statusPublished - 2015 May 4
Externally publishedYes


  • Bayesian calibration
  • energy audit
  • energy simulation model
  • uncertainty analysis

ASJC Scopus subject areas

  • Architecture
  • Building and Construction
  • Modelling and Simulation
  • Computer Science Applications


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