Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
This work addresses the problem of inefficient and complex solar PV potential assessment for urban planners and energy analysts, representing an incremental improvement through automation and integration of existing AI techniques.
This study tackled the challenge of assessing solar photovoltaic (PV) installation potential on building facades in dense urban environments by introducing SF-SPA, an automated framework that processes street-view photographs, achieving a mean area estimation error of 6.2% ± 2.8% and reducing assessment time to about 100 seconds per building.
Building facades represent a significant untapped resource for solar energy generation in dense urban environments, yet assessing their photovoltaic (PV) potential remains challenging due to complex geometries and semantic com ponents. This study introduces SF-SPA (Semantic Facade Solar-PV Assessment), an automated framework that transforms street-view photographs into quantitative PV deployment assessments. The approach combines com puter vision and artificial intelligence techniques to address three key challenges: perspective distortion correction, semantic understanding of facade elements, and spatial reasoning for PV layout optimization. Our four-stage pipeline processes images through geometric rectification, zero-shot semantic segmentation, Large Language Model (LLM) guided spatial reasoning, and energy simulation. Validation across 80 buildings in four countries demonstrates ro bust performance with mean area estimation errors of 6.2% ± 2.8% compared to expert annotations. The auto mated assessment requires approximately 100 seconds per building, a substantial gain in efficiency over manual methods. Simulated energy yield predictions confirm the method's reliability and applicability for regional poten tial studies, urban energy planning, and building-integrated photovoltaic (BIPV) deployment. Code is available at: https:github.com/CodeAXu/Solar-PV-Installation