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Informing building retrofits at low computational costs: a multi-objective optimisation using machine learning surrogates of building performance simulation models

Author(s): (Department of Civil and Environmental Engineering, Carleton University, Ottawa, Canada)
(Department of Civil and Environmental Engineering, Carleton University, Ottawa, Canada)
(Department of Civil Engineering, IIT Bombay, Powai, Mumbai, India)
(Department of Civil Engineering, IIT Bombay, Powai, Mumbai, India)
(Department of Civil Engineering, IIT Bombay, Powai, Mumbai, India)
(Department of Civil Engineering, IIT Bombay, Powai, Mumbai, India)
ORCID (Department of Civil and Environmental Engineering, Carleton University, Ottawa, Canada)
Medium: journal article
Language(s): English
Published in: Journal of Building Performance Simulation
Page(s): 1-17
DOI: 10.1080/19401493.2024.2384487
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: 10.1080/19401493.2024.2384487.
  • About this
    data sheet
  • Reference-ID
    10797276
  • Published on:
    01/09/2024
  • Last updated on:
    01/09/2024
 
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