Evaluation of calibration efficacy under different levels of uncertainty
Abstract
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.
- Authors:
-
- Univ. of Cambridge, Cambridge (United Kingdom)
- Argonne National Lab. (ANL), Lemont, IL (United States)
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- OSTI Identifier:
- 1391908
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Building Performance Simulation
- Additional Journal Information:
- Journal Volume: 8; Journal Issue: 3; Journal ID: ISSN 1940-1493
- Publisher:
- Taylor & Francis
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Bayesian Calibration; Building Energy; Energy Audit; Energy Simulation Model; Uncertainty Analysis
Citation Formats
Heo, Yeonsook, Graziano, Diane J., Guzowski, Leah, and Muehleisen, Ralph T. Evaluation of calibration efficacy under different levels of uncertainty. United States: N. p., 2014.
Web. doi:10.1080/19401493.2014.896947.
Heo, Yeonsook, Graziano, Diane J., Guzowski, Leah, & Muehleisen, Ralph T. Evaluation of calibration efficacy under different levels of uncertainty. United States. https://doi.org/10.1080/19401493.2014.896947
Heo, Yeonsook, Graziano, Diane J., Guzowski, Leah, and Muehleisen, Ralph T. Tue .
"Evaluation of calibration efficacy under different levels of uncertainty". United States. https://doi.org/10.1080/19401493.2014.896947. https://www.osti.gov/servlets/purl/1391908.
@article{osti_1391908,
title = {Evaluation of calibration efficacy under different levels of uncertainty},
author = {Heo, Yeonsook and Graziano, Diane J. and Guzowski, Leah and Muehleisen, Ralph T.},
abstractNote = {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.},
doi = {10.1080/19401493.2014.896947},
journal = {Journal of Building Performance Simulation},
number = 3,
volume = 8,
place = {United States},
year = {Tue Jun 10 00:00:00 EDT 2014},
month = {Tue Jun 10 00:00:00 EDT 2014}
}
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Works referencing / citing this record:
Review on stochastic modeling methods for building stock energy prediction
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Influence of error terms in Bayesian calibration of energy system models
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Influence of error terms in Bayesian calibration of energy system models
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