A novel method for assessment rooftop PV potential based on remote sensing images

Abstract

The assessment of rooftop photovoltaic (PV) potential is highly significant for energy policy formulation. With the rapid development of computer vision (CV) and remote sensing imagery, utilizing CV to extract rooftop information is an ideal approach. However, deep learning requires a large amount of accurately annotated data, and annotating remote sensing images is a labor-intensive task. This limitation hinders the application of deep learning in rooftop PV potential assessment. To address this issue, this paper proposes a semi-supervised learning (SSL)-based segmentation model to extract rooftop information from remote sensing images. Subsequently, a rooftop classification method is proposed to categorize rooftops into several classes and estimate their rooftop PV available area ratios. Finally, the total available rooftop PV area in urban areas is evaluated, and the potential rooftop PV installed capacity and power generation are calculated. This method is applied in the Longhu District of Shantou City, Guangdong Province. The evaluation results show that the total rooftop area in Longhu District is 17.2 km², with a rooftop PV available area of 12.7 km². It is estimated that the rooftop PV installed capacity in Longhu District is 1849.4 MW, with an annual power generation of 2219.3 GWh.