Treffer: AI-powered automated building façade segmentation and BIPV system potential prediction using CycleGAN and PVGIS.

[Display omitted] • Unsupervised facade segmentation using CycleGAN with unpaired image data. • Automated retrieval of site-specific solar data via the PVGIS API. • BIPV system sizing and performance assessment. • End-to-end framework by linking facade segmentation to PVGIS energy yield prediction. • PVsyst Validation for technical accuracy and system viability. Building-integrated photovoltaics (BIPV) represents a promising pathway for advancing urban sustainability. Accurately identifying suitable façade surfaces is essential for maximising opportunities for BIPV integration. This study develops a novel AI-powered framework based on Cycle-Consistent Generative Adversarial Network (CycleGAN) to segment building façade regions suitable for BIPV installation. The developed unsupervised learning approach enables unpaired image-to-image translation between real-world facades and their corresponding segmentation masks, thus eliminating the need for pixel-level annotations, reducing reliance on manually labelled datasets, and minimising system sizing time. The resulting façade segmentation mask was post-processed and used as input to PVGIS, accessed via its application programming interface (API) through Python for energy yield prediction, while PVsyst simulation was employed for PVGIS validation. A case study conducted in Edinburgh (Lat/Lon 55.933, −3.213) for a south-facing façade demonstrated the model's ability to identify 81 m2 of usable BIPV areas, representing 41.55% of the total surface. The CycleGAN model achieved an intersection over union (IoU) of 0.78 and a Dice coefficient of 0.88, confirming stable adversarial learning. The end-to-end processing time per façade image ranged from 2 to 5 s. The results showed close agreement between both tools, with annual energy generation values of 11.8 MWh (PVGIS) and 11.5 MWh (PVsyst), corresponding to a relative deviation of approximately 2.5%. The findings highlight the practicality, scalability, and cost-effectiveness of integrating AI-façade segmentation with energy simulation tools for early-stage BIPV assessment. This integrated workflow provides a foundation for urban BIPV planning and pre-feasibility studies, supporting innovative renewable integration within city infrastructure. [ABSTRACT FROM AUTHOR]