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Federated learning for enhancing extrapolation ability of HVAC models: case study on two real-life DOAS units.

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Title: Federated learning for enhancing extrapolation ability of HVAC models: case study on two real-life DOAS units.
Authors: Cho, Seongkwon1 (AUTHOR) gyflsnu@snu.ac.kr, Park, Cheol Soo2 (AUTHOR)
Source: Journal of Building Performance Simulation. Jun2026, Vol. 19 Issue 4, p622-637. 16p.
Subjects: Federated learning, Extrapolation, Independent variables, Artificial neural networks, Ventilation, Statistical models
Abstract: Conventional data-driven modeling often fails under unseen conditions. This study introduces bidirectional knowledge sharing via federated learning (FL) across multiple systems without data exchange. The method was tested on two dedicated outdoor air system (DOAS) units with imbalanced datasets. FL improved sensible heat prediction accuracy, reducing CVRMSE from 39.1 to 17.7% for DOAS 1 and 13.9 to 9.8% for DOAS 2 compared to a conventional ANN. Under unseen control conditions, FL further reduced CVRMSE from 33.1 to 15.1% for DOAS 1 and 21.3 to 14.6% for DOAS 2, demonstrating enhanced extrapolation. Intervention-based analyses also showed that FL promotes more physically consistent relationships between control variables and system responses. Although limited to a short-term case study of two similar systems, the results highlight FL's potential to improve the robustness of data-driven HVAC models. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Federated learning for enhancing extrapolation ability of HVAC models: case study on two real-life DOAS units.
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  Data: <searchLink fieldCode="AR" term="%22Cho%2C+Seongkwon%22">Cho, Seongkwon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gyflsnu@snu.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Park%2C+Cheol+Soo%22">Park, Cheol Soo</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Building+Performance+Simulation%22">Journal of Building Performance Simulation</searchLink>. Jun2026, Vol. 19 Issue 4, p622-637. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Extrapolation%22">Extrapolation</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+variables%22">Independent variables</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Ventilation%22">Ventilation</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Conventional data-driven modeling often fails under unseen conditions. This study introduces bidirectional knowledge sharing via federated learning (FL) across multiple systems without data exchange. The method was tested on two dedicated outdoor air system (DOAS) units with imbalanced datasets. FL improved sensible heat prediction accuracy, reducing CVRMSE from 39.1 to 17.7% for DOAS 1 and 13.9 to 9.8% for DOAS 2 compared to a conventional ANN. Under unseen control conditions, FL further reduced CVRMSE from 33.1 to 15.1% for DOAS 1 and 21.3 to 14.6% for DOAS 2, demonstrating enhanced extrapolation. Intervention-based analyses also showed that FL promotes more physically consistent relationships between control variables and system responses. Although limited to a short-term case study of two similar systems, the results highlight FL's potential to improve the robustness of data-driven HVAC models. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/19401493.2026.2637735
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 622
    Subjects:
      – SubjectFull: Federated learning
        Type: general
      – SubjectFull: Extrapolation
        Type: general
      – SubjectFull: Independent variables
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Ventilation
        Type: general
      – SubjectFull: Statistical models
        Type: general
    Titles:
      – TitleFull: Federated learning for enhancing extrapolation ability of HVAC models: case study on two real-life DOAS units.
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            NameFull: Cho, Seongkwon
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            NameFull: Park, Cheol Soo
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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