Evaluation of calibration efficacy under different levels of uncertainty

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

doi:10.1080/19401493.2014.896947

Evaluation of calibration efficacy under different levels of uncertainty

Journal Article · Tue Jun 10 00:00:00 EDT 2014 · Journal of Building Performance Simulation

DOI:https://doi.org/10.1080/19401493.2014.896947· OSTI ID:1391908

Heo, Yeonsook ^[1]; Graziano, Diane J. ^[2]; Guzowski, Leah ^[2]; Muehleisen, Ralph T. ^[2]

Univ. of Cambridge, Cambridge (United Kingdom)
Argonne National Lab. (ANL), Lemont, IL (United States)

This study 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1391908

Journal Information:: Journal of Building Performance Simulation, Journal Name: Journal of Building Performance Simulation Journal Issue: 3 Vol. 8; ISSN 1940-1493

Publisher:: Taylor & FrancisCopyright Statement

Country of Publication:: United States

Language:: English

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97 MATHEMATICS AND COMPUTING
Bayesian Calibration
Building Energy
Energy Audit
Energy Simulation Model
Uncertainty Analysis

Evaluation of calibration efficacy under different levels of uncertainty

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