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An indoor airflow distribution predictor using machine learning for a real-time healthy building monitoring system in the tropics

Author(s): ORCID (Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Yogyakarta, Indonesia)
ORCID (Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Yogyakarta, Indonesia)
ORCID (Institute for Environmental Design and Engineering, University College London, London, UK)
ORCID (Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Yogyakarta, Indonesia)
ORCID (Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Yogyakarta, Indonesia)
ORCID (Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Yogyakarta, Indonesia)
Medium: journal article
Language(s): English
Published in: Building Services Engineering Research and Technology, , n. 3, v. 45
Page(s): 293-315
DOI: 10.1177/01436244241231354
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/01436244241231354.
  • About this
    data sheet
  • Reference-ID
    10760901
  • Published on:
    23/03/2024
  • Last updated on:
    20/09/2024
 
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