This project evaluates the suitability of 3D interior space models acquired using Apple’s RoomPlan API for daylight simulations. The main contribution is a Python-based tool that converts RoomPlan output into HoneyBeeJSON by automatically reconstructing the ceiling and adding window frames, which are missing in RoomPlan’s output. Although RoomPlan is also able to capture furniture, these elements were not used in the geometric evaluation or in the daylight simulations. The resulting models can be directly used in Grasshopper for daylight simulations, reducing the modeling time required by practitioners. To assess the suitability of RoomPlan, three office interiors were scanned using a TLS and modeled both manually and with an iPhone 12 Pro.
The manual models were used as ground truth. For each room, a geometric evaluation and a daylight simulation evaluation were performed using three model versions: manual, RoomPlan with extruded window frames, and RoomPlan without extruded window frames. For both the geometrical and the daylight performance evaluation, it is apparent that the windows' frames extrusion is significant to achieve more accurate results. Geometric accuracy was evaluated using Chamfer and Hausdorff distances, showing good overall accuracy. However, errors were observed in wall heights when the ceiling was not clearly visible and in the separation of windows located close to each other. The models were used for point-in-time grid-based illuminance and view-based luminance simulations in Grasshopper using Honeybee.
For the illuminance simulations, the MAE is approximately 269 lux and the MAPE is 19.5%. For DGP, the MAPE is 7.6% for the RoomPlan models with extruded window frames, with only one misclassification of the DGP category. The results indicate that RoomPlan can be used for visual comfort studies but not for daylight availability studies. Despite these results, suggestions for further work are given, considering both the geometrical and the daylight simulation performance evaluation of the RoomPlan models. ... Read more

As living in urban environments increases, the impact of city design on the health of individuals becomes more relevant. Cities have shown to negatively affect individuals' circadian health. This thesis investigates the influence of façade and city lighting design on indoor circadian lighting availability, addressing the increasing prevalence of circadian disruption in cities caused by poor daylight access and excessive artificial light exposure.
The research introduces a workflow that evaluates how façade designs and urban lighting choices affect circadian light exposure within homes based on open data sources. By creating a tool capable of simulating these effects for individual floor levels on an urban scale, the study provides a method to assess and optimize architectural and urban design for circadian health with public information in a large-scale manner.
The results aim to bridge the gap between available 3D urban data and practical applications, offering design strategies that improve indoor circadian lighting availability and contribute to healthier urban living environments.
Key findings prove the misalignment of indoor circadian lighting availability with human needs in cities, and show the importance of window size, floor level and façade orientation in mitigating circadian disruption caused by the urban context. Next to that, it shows the negative impact city lighting has on indoor circadian health, for which design suggestions are done.
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