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Title: Evaluation of calibration efficacy under different levels of uncertainty

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:
 [1] ;  [2] ;  [2] ;  [2]
  1. Univ. of Cambridge, Cambridge (United Kingdom)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
Publication Date:
Grant/Contract Number:
AC02-06CH11357
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
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Bayesian Calibration; Building Energy; Energy Audit; Energy Simulation Model; Uncertainty Analysis
OSTI Identifier:
1391908