  This research is mainly focused on the assessment of machine learning
algorithms in the prediction of daylight and visual comfort metrics in the
early design stages. A dataset was primarily developed from 2880 simulations
derived from Honeybee for Grasshopper. The simulations were done for a shoebox
space with a one side window. The alternatives emerged from different physical
features, including room dimensions, interior surfaces reflectance, window
dimensions and orientations, number of windows, and shading states. 5 metrics
were used for daylight evaluations, including UDI, sDA, mDA, ASE, and sVD.
Quality Views were analyzed for the same shoebox spaces via a grasshopper-based
algorithm, developed from the LEED v4 evaluation framework for Quality Views.
The dataset was further analyzed with an Artificial Neural Network algorithm
written in Python. The accuracy of the predictions was estimated at 97% on
average. The developed model could be used in early design stages analyses
without the need for time-consuming simulations in previously used platforms
and programs.
