Evaluation of calibration efficacy under different levels of uncertainty

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

Research output: Contribution to conferencePaper

8 Citations (Scopus)

Abstract

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 models. A Bayesian approach can quantify uncertainty in uncertain parameters while updating their values given measurement data. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables researchers to rigorously validate the accuracy of calibration results in terms of both calibration parameter values and calibrated 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
Pages1690-1697
Number of pages8
Publication statusPublished - 2013
Event13th Conference of the International Building Performance Simulation Association, BS 2013 - Chambery, France
Duration: 2013 Aug 262013 Aug 28

Conference

Conference13th Conference of the International Building Performance Simulation Association, BS 2013
CountryFrance
CityChambery
Period13/8/2613/8/28

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Modelling and Simulation

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  • Cite this

    Heo, Y., Graziano, D., Guzowski, L., & Muehleisen, R. T. (2013). Evaluation of calibration efficacy under different levels of uncertainty. 1690-1697. Paper presented at 13th Conference of the International Building Performance Simulation Association, BS 2013, Chambery, France.