Norhan Bayomi, Mohanned El Kholy, John E. Fernandez, Senem Velipasalar, Tarek Rakha

Research output: Contribution to journalConference Articlepeer-review


Building performance significantly influences energy use and indoor thermal conditions tied to the quality of living for its occupants. Therefore, information on building envelopes is essential, especially considering that envelopes and windows can impact 50% of energy loads in the United States. However, current retrofits supporting Building Energy Modelling (BEM) tools face multiple barriers, including time consumption and labor intensity due to manual modeling and calibration processes. This paper proposes using Deep Learning (DL) -based object detection algorithms to detect building envelope components, more specificlly doors, and windows, that can be applied to building energy performance analysis, 3D modeling, and assessment of thermal irregularities. We compare four different versions of the state-of-the-art YOLO V5 model to identify which version best suits the goal of detecting these building components. Results show that YOLO V5_X provides the best performance for detection accuracy.

Original languageEnglish (US)
Pages (from-to)143-156
Number of pages14
JournalSimulation Series
Issue number1
StatePublished - 2022
Event2022 Annual Modeling and Simulation Conference, ANNSIM 2022 - San Diego, United States
Duration: Jul 18 2022Jul 20 2022


  • BEM
  • artificial neural networks
  • building performance
  • deep learning
  • object detection

ASJC Scopus subject areas

  • Computer Networks and Communications


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