Daylighting AI
AI for analyzing and optimizing natural light in buildings.
Definition
Daylighting AI uses simulation and machine learning to analyze and optimize natural light in building designs. These systems can predict daylight levels throughout spaces, evaluate glare conditions, optimize window sizing and shading, and ensure designs meet daylighting requirements for energy codes and green building certifications.
In Depth
Daylighting analysis evaluates how natural light enters and distributes through building spaces, affecting both energy consumption (reduced electric lighting) and occupant well-being (access to daylight and views). AI makes daylighting analysis practical during early design when the most impactful decisions — building orientation, floor plate depth, window placement, and shading — are being made.
The analysis calculates spatial Daylight Autonomy (sDA) — the percentage of floor area that receives adequate daylight for a specified percentage of annual occupied hours. AI models the daylight distribution for different facade configurations, calculating sDA and Annual Sunlight Exposure (ASE) to meet LEED and WELL daylighting credits while avoiding glare conditions that would make the spaces uncomfortable.
Examples
Analyzing daylight autonomy for LEED credits
Optimizing window placement for daylighting
Evaluating shading strategies for glare control
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Daylighting AI uses simulation and machine learning to analyze and optimize natural light in building designs. These systems can predict daylight levels throughout spaces, evaluate glare conditions, optimize window sizing and shading, and ensure designs meet daylighting requirements for energy codes and green building certifications.
Analyzing daylight autonomy for LEED credits. Optimizing window placement for daylighting. Evaluating shading strategies for glare control.
Automated Code Compliance: Check drawings against 380+ building codes and standards with cited answers.