Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach

Abstract

Solar energy shines as a beacon for sustainable development, with rooftop solar photovoltaic (PV) installations playing a crucial role. This study proposes a novel framework to precisely assess citywide existing solar power generation and analyze future potential under various rooftop utilization scenarios (10–50 %). To illustrate the methodology, the existing solar PV yield and future potential of Islamabad are explored as a case study. Employing open-source satellite data and deep learning (DL), this study quantified the electricity generation from current solar infrastructure and projected future possibilities. DL models like Mask R–CNN and Deeplab-v3 are trained on extensive custom datasets for solar panels and rooftops extraction. The study delineated 19,000 solar arrays, covering an area of 0.8 sqkm, and extracted 75.08 sqkm of suitable rooftops for future installations. The analysis revealed, current solar infrastructure generates 141.42 GWh electricity, satisfying 6.34 % of Islamabad's annual energy demand. Utilizing 50 % rooftop area could generate 6578 GWh annually, meeting 294 % of the city's electricity needs. This research contributes to the strategic planning for solar energy infrastructure, demonstrating the substantial role rooftop solar can play in meeting urban energy needs. This can inform policy decisions and guide investment opportunities for expanding clean energy in the city.