This research is an early exploration into the potential of deep neural networks predicting physically-based building performance metrics on 3D urban geometry. Specifically, this work aims to predict annual solar irradiation using networks trained on point clouds. It is expected that the proposed method will allow designers to optimize their designs based on solar performance, due to the significantly lower inference time, in comparison to traditional simulation models. Furthermore, this research paves the way for generative models, which include the evaluation of performance such as solar irradiation, wind and acoustics.

Prior research has suggested several methods to predict solar irradiation on 2D building data and low resolution 3D buildings. These researches have in common that there is a lack of real observed irradiation data on buildings. Therefore, this research proposes several methods to efficiently synthesize an irradiation dataset based on 3D building geometry, which samples are larger in scale and resolution in comparison to earlier work.

Based on the synthesized datasets, several models have been trained to predict the irradiation values. Experiments have shown that a finetuned version of the model is able to predict irradiation with an average RMSE of 21 kWh/m2 with an average inference time of 0.7 seconds, on a patch of 100x100 meters. Furthermore, this thesis provides an analysis on how patch size and point sampling technique affect the prediction error of the model.

Finally, this research provides an implementation of the network in a user-friendly client-server ecosystem, that can assist architects and engineers to fine-tune their building designs in urban environments.