Utilizing High Performance Computing to Improve the Application of Machine Learning for Time-Efficient Prediction of Buildings Daylighting Performance | Academic Article individual record

Abstract Architects often investigate the daylighting performance of hundreds of design solutions and configurations to ensure an energy-efficient solution for their designs. To shorten the time required for daylighting simulations, architects usually reduce the number of variables or parameters of the building and facade design. This practice usually results in the elimination of design variables that could contribute to an energy-optimized design configuration. Therefore, recent research has focused on incorporating machine learning algorithms that require the execution of only a relatively small subset of the simulations to predict the daylighting and energy performance of buildings. Although machine learning has been shown to be accurate, it still becomes a time-consuming process due to the time required to execute a set of simulations to be used as training and validation data. Furthermore, to save time, designers often decide to use a small simulation subset, which leads to a poorly designed machine learning algorithm that produces inaccurate results. Therefore, this study aims to introduce an automated framework that utilizes high performance computing (HPC) to execute the simulations necessary for the machine learning algorithm while saving time and effort. High performance computing facilitates the execution of thousands of tasks simultaneously for a time-efficient simulation process, therefore allowing designers to increase the size of the simulations subset. Pairing high performance computing with machine learning allows for accurate and nearly instantaneous building performance predictions.

publication outlet

Journal of Physics Conference Series

author list (cited authors)
Labib, R.
publication date
IOP Publishing Publisher
  • Bps
  • High Performance Computing
  • Deep Learning
  • Parametric Daylighting
  • Daylight Autonomy (da)
  • Machine Learning
  • Ibpsa
  • Machine Learning For Daylighting
  • Neural Network
  • Daylighting
  • Building Performance Simulations
citation count


Digital Object Identifier (DOI)
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