Ensemble machine learning framework for daylight modelling of various building layouts

Rashed Alsharif, Mehrdad Arashpour, Emad Golafshani, Milad Bazli, Saeed Reza Mohandes

Research output: Contribution to journalArticleResearchpeer-review


The application of machine learning (ML) modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment. Although many advancements have been made, no standardized ML modelling framework exists in daylight assessment. In this study, 625 different building layouts were generated to model useful daylight illuminance (UDI). Two state-of-the-art ML algorithms, eXtreme Gradient Boosting (XGBoost) and random forest (RF), were employed to analyze UDI in four categories: UDI-f (fell short), UDI-s (supplementary), UDI-a (autonomous), and UDI-e (exceeded). A feature (internal finish) was introduced to the framework to better reflect real-world representation. The results show that XGBoost models predict UDI with a maximum accuracy of R 2 = 0.992. Compared to RF, the XGBoost ML models can significantly reduce prediction errors. Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction.

Original languageEnglish
Pages (from-to)2049–2061
Number of pages13
JournalBuilding Simulation
Publication statusPublished - 30 Aug 2023


  • artificial intelligence
  • indoor environment
  • machine learning
  • parametric building layout
  • sunlight
  • visual comfort

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