Orientation
South · East · West
KTI · Daylight–Energy Performance Analytics
Facade performance for a modular office building.
A Grasshopper shoebox model and ClimateStudio simulations compare seven window designs, three orientations and several external-louvre settings.
648configurations
5input variables
3performance outputs
01 · Project setup
Building and simulation model.
This one-week study used a modular 37 × 14 m office building and repeated 3.7 × 5.4 m rooms as a consistent simulation model. Its purpose was to help design teams identify a strong pool of facade options using predetermined variables agreed through workshops: orientation, window design and louvre geometry. Each run connected those inputs to total load, cooling load and useful daylight illuminance at desk level.
02 · Parameters
Five variables define each run.
Orientation, window design and three louvre settings define each configuration. The depth-zero cases retain one canonical gaps and rotation setting to avoid repeating identical no-louvre simulations.
Louvre gaps
5 · 6 · 7
Rotation
−40° · −20° · 0° · 20° · 40°
Depth
0 · 20 · 40
Window design
Design 0 to design 6
03 · Results
Filter and compare the configurations.
Use the first row of controls to define a set and the second row to create a direct comparison. Select a point to load its exact facade image and input settings, or isolate the five ranked options for each orientation.
First selection
Comparison
Shared
Top five options when enabled
Other configurations (20%)
Loading the results explorer…
Total load vs. UDI at desk levelSelect a point to see its facade
648 configurationsPerformance by orientationShared range is green; differences retain each selection colour
Selected configuration
Select a point in the scatter plot.
Input settings and output values will appear here.
Total load—
Cooling load—
UDI at desk level—
04 · Reading the dataset
Energy and daylight do not produce one universal optimum.
The ranking identifies five options per orientation after applying a minimum UDI requirement of 55%, weighting lower cooling load at 70% and higher UDI at 30%. Fixed graph limits keep filtered groups comparable with the complete dataset, while the paired filters show how individual design decisions change performance.