Evaluation of the Efficacy of Bayesian Calibration: Interim Project Report

Technical Report · Wed Jan 01 04:00:00 EST 2014

DOI:https://doi.org/10.2172/1130299· OSTI ID:1130299

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

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1. Argonne National Laboratory (ANL), Argonne, IL (United States)

This project evaluated the efficacy of applying Bayesian calibration to EnergyPlus building energy models under different levels of data uncertainty. Small, medium, and large office buildings were modeled with input uncertainty consistent with data collection from the three standard ASHRAE audit levels. It was shown that Bayesian calibration both adjusts parameter values closer to the true values, while simultaneously reducing the uncertainty in the parameter values. Consequently, Bayesian-calibrated EnergyPlus models enhance the reliability of energy use predictions by more closely matching monthly utility bills with much reduced uncertainty compared with uncalibrated models.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Contributing Organization: University of Cambridge
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1130299
• Report Number(s): ANL/DIS--14/1
• Country of Publication: United States
• Language: ENGLISH