Rapid estimation of city-scale PV potential via 3D morphology and multi-source data fusion

Urban building rooftops represent a high-potential source of solar PV power. However, prevalent estimation methods often overlook shading from surrounding structures, causing inaccuracies. This study proposes a method to estimate rooftop PV potential by integrating open-source multi-modal spatiotemporal data. By calculating solar radiation on building rooftops under both planar and 3D conditions, we derived a solar occlusion correction ratio. Using the morphological characteristics of buildings in Kunming, we established a random forest regression model to explore the impact of shading on rooftop solar radiation. After determining the correction ratio, our method achieves second-scale estimation of solar PV potential from planar conditions to those considering 3D morphological shading, reducing the global relative error from 3.66% to 0.04% (DSM-based benchmarks; 3.66% : FLAT-based; 0.04% : our ratio-based). This research includes the creation of solar radiation maps for the main areas in Kunming, analysis of PV generation potential, energy balance, and carbon emission reduction benefits, with Wuhan further used as a test city to validate the model. The findings provide valuable insights for future policy-making on PV installations, energy consumption, and grid emissions.