Rapid assessment of solar potential for building surfaces in complex urban morphologies based on vector processing

Solar energy in the urban environment serves as a promising solution to address the challenges of increasing electricity demand amidst climate change and extreme weather events. Yet, the assessment of urban solar energy potential faces a critical trade-off between accuracy and computational efficiency, where existing methods either oversimplify building interactions or become computationally prohibitive at scale. We present a vector-based algorithm for rapid urban-scale solar potential calculations that achieves high accuracy with minimal computational requirements. The method achieves facade-level solar radiation estimates with 99.83% accuracy (mean absolute percentage error: 0.17%) compared to traditional simulation approaches, while reducing computation time by two orders of magnitude (0.05-0.62 s per building depending on the data size). Validated through case studies in San Francisco’s diverse urban morphologies, the algorithm efficiently handles complex building geometries and mutual shading effects. This advancement enables high-resolution solar potential assessment at an urban scale, facilitating subsequent research in urban energy modeling and distributed energy planning applications, and paving the way for accurate and efficient urban energy planning and sustainable development strategies.