TY - JOUR
T1 - Evaluation of calibration efficacy under different levels of uncertainty
AU - Heo, Yeonsook
AU - Graziano, Diane J.
AU - Guzowski, Leah
AU - Muehleisen, Ralph T.
N1 - Funding Information:
This work was supported by the US Department of Energy under Contract No. DE-AC02-06CH11357.
Funding Information:
The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
Publisher Copyright:
© 2014, International Building Performance Simulation Association (IBPSA).
PY - 2015/5/4
Y1 - 2015/5/4
N2 - 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.
AB - 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.
KW - Bayesian calibration
KW - energy audit
KW - energy simulation model
KW - uncertainty analysis
UR - http://www.scopus.com/inward/record.url?scp=84926248061&partnerID=8YFLogxK
U2 - 10.1080/19401493.2014.896947
DO - 10.1080/19401493.2014.896947
M3 - Article
AN - SCOPUS:84926248061
VL - 8
SP - 135
EP - 144
JO - Journal of Building Performance Simulation
JF - Journal of Building Performance Simulation
SN - 1940-1493
IS - 3
ER -