Improving the resolution of solar energy potential maps derived from global DSMs for rooftop solar panel placement using deep learning - Maryam Hosseini; Hossein Bagheri (Heliyon)
This study focuses on generating high-resolution annual solar energy potential maps (ASMs) using global Digital Elevation Models (DEMs) to aid in solar panel placement, especially in urban areas. A framework was developed to enhance the resolution of these maps. Initially, the accuracy of ASMs derived from various DEMs was compared with LiDAR-derived ASMs. The evaluations indicated that the Copernicus DEM provided a highly accurate ASM. Subsequently, deep learning algorithms were trained to improve the resolution of the LiDAR-derived ASM. The results demonstrated that the Enhanced Deep Super-Resolution (EDSR) Network outperformed the U-Net-based model. The trained EDSR model was then utilized to enhance the resolution of the Copernicus ASM. Comparing the enhanced-resolution map of Copernicus respective to LiDAR showed that the EDSR model provided the necessary generalizability to improve the accuracy and resolution of the Copernicus ASM, particularly in urban areas. The investigations revealed that the improved resolution map with a resolution of 6 m, achieving RMSE of 35.75 MWhm2 and a correlation of 0.87 respective to LiDAR data, was capable of locating solar panels on buildings, whereas the original Copernicus-derived maps with a 30 m resolution had RMSE of 51.26 MWhm2 and a correlation of 0.72 for such placement purposes.