Multiblock Parameter Calibration in Computer Models

Cheoljoon Jeong (), Ziang Xu (), Albert S. Berahas (), Eunshin Byon () and Kristen Cetin ()
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Cheoljoon Jeong: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Ziang Xu: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Albert S. Berahas: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Eunshin Byon: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Kristen Cetin: Department of Civil and Environmental Engineering, Michigan State University, East Lansing, Michigan 48824

INFORMS Joural on Data Science, 2023, vol. 2, issue 2, 116-137

Abstract: Parameter calibration aims to estimate unobservable parameters used in a computer model by using physical process responses and computer model outputs. In the literature, existing studies calibrate all parameters simultaneously using an entire data set. However, in certain applications, some parameters are associated with only a subset of data. For example, in the building energy simulation, cooling (heating) season parameters should be calibrated using data collected during the cooling (heating) season only. This study provides a new multiblock calibration approach that considers such heterogeneity. Unlike existing studies that build emulators for the computer model response, such as the widely used Bayesian calibration approach, we consider multiple loss functions to be minimized, each for a block of parameters that use the corresponding data set, and estimate the parameters using a nonlinear optimization technique. We present the convergence properties under certain conditions and quantify the parameter estimation uncertainties. The superiority of our approach is demonstrated through numerical studies and a real-world building energy simulation case study.

Keywords: building energy; nonlinear optimization; simulation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:2:y:2023:i:2:p:116-137

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