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 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 language | English |
|---|---|
| Pages (from-to) | 135-144 |
| Number of pages | 10 |
| Journal | Journal of Building Performance Simulation |
| Volume | 8 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2015 May 4 |
| Externally published | Yes |
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Keywords
- Bayesian calibration
- energy audit
- energy simulation model
- uncertainty analysis
ASJC Scopus subject areas
- Architecture
- Building and Construction
- Modelling and Simulation
- Computer Science Applications
Cite this
Evaluation of calibration efficacy under different levels of uncertainty. / Heo, Yeonsook; Graziano, Diane J.; Guzowski, Leah; Muehleisen, Ralph T.
In: Journal of Building Performance Simulation, Vol. 8, No. 3, 04.05.2015, p. 135-144.Research output: Contribution to journal › Article
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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.
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
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U2 - 10.1080/19401493.2014.896947
DO - 10.1080/19401493.2014.896947
M3 - Article
VL - 8
SP - 135
EP - 144
JO - Journal of Building Performance Simulation
JF - Journal of Building Performance Simulation
SN - 1940-1493
IS - 3
ER -