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Multiblock Parameter Calibration in Computer Models

Author

Listed:
  • 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)

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.

Suggested Citation

  • Cheoljoon Jeong & Ziang Xu & Albert S. Berahas & Eunshin Byon & Kristen Cetin, 2023. "Multiblock Parameter Calibration in Computer Models," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 116-137, October.
    • Handle: RePEc:inm:orijds:v:2:y:2023:i:2:p:116-137
    DOI: 10.1287/ijds.2023.0029
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    References listed on IDEAS

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    1. Eunshin Byon & Abhishek Shrivastava & Yu Ding, 2010. "A classification procedure for highly imbalanced class sizes," IISE Transactions, Taylor & Francis Journals, vol. 42(4), pages 288-303.
    2. Mingdi You & Eunshin Byon & Jionghua (Judy) Jin & Giwhyun Lee, 2017. "When wind travels through turbines: A new statistical approach for characterizing heterogeneous wake effects in multi-turbine wind farms," IISE Transactions, Taylor & Francis Journals, vol. 49(1), pages 84-95, January.
    3. Manfren, Massimiliano & Aste, Niccolò & Moshksar, Reza, 2013. "Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation," Applied Energy, Elsevier, vol. 103(C), pages 627-641.
    4. Michael F. Howland & Jesús Bas Quesada & Juan José Pena Martínez & Felipe Palou Larrañaga & Neeraj Yadav & Jasvipul S. Chawla & Varun Sivaram & John O. Dabiri, 2022. "Collective wind farm operation based on a predictive model increases utility-scale energy production," Nature Energy, Nature, vol. 7(9), pages 818-827, September.
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