Hosseini Maryam, Bagheri Hossein
Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran.
Heliyon. 2024 Dec 13;11(1):e41193. doi: 10.1016/j.heliyon.2024.e41193. eCollection 2025 Jan 15.
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 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 and a correlation of 0.72 for such placement purposes.
本研究聚焦于利用全球数字高程模型（DEM）生成高分辨率年度太阳能潜力地图（ASM），以辅助太阳能板的放置，特别是在城市地区。开发了一个框架来提高这些地图的分辨率。最初，将源自各种DEM的ASM的准确性与源自激光雷达的ASM进行了比较。评估表明，哥白尼DEM提供了高度准确的ASM。随后，训练深度学习算法以提高源自激光雷达的ASM的分辨率。结果表明，增强深度超分辨率（EDSR）网络优于基于U-Net的模型。然后利用训练好的EDSR模型来提高哥白尼ASM的分辨率。将哥白尼的增强分辨率地图与激光雷达的地图进行比较表明，EDSR模型提供了必要的通用性，以提高哥白尼ASM的准确性和分辨率，特别是在城市地区。调查显示，分辨率为6米的改进分辨率地图相对于激光雷达数据的均方根误差（RMSE）为35.75，相关性为0.87，能够在建筑物上定位太阳能板，而原始的30米分辨率的哥白尼派生地图用于此类放置目的时的RMSE为51.26，相关性为0.72。
