DaylightPredictor
(by Reinhardt Swart)

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With this script, designers can use block massing or programming gels with real time daylighting feedback. We focus on Useful Daylight Illuminance and is climate specific to Los Angeles. Using a parametric roombox, the model is trained on 16,000 iterations via ClimateStudio and leverages an artificial neural network (ANN) for UDI prediction. The idea is to provide real time LOD 2.5 daylighting performance feedback for typical room and facade configurations.

Please visit our Github page for full installation instructions. To run the script, we are using Cpython via Flask/Hops, as shown here: https://github.com/mcneel/compute.rhino3d/tree/8.x/src/ghhops-server-py

The script requires other plugins including HumanUI, Urbano, and Anemone.

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