Researchers from China University of Mining & Technology and collaborating institutions have developed a novel framework for optimizing solar photovoltaic (PV) panel layout using compressed LiDAR point cloud data, a breakthrough poised to significantly enhance the efficiency and accuracy of site selection for large-scale solar projects. The study, provisionally accepted for publication in Frontiers in Earth Science, addresses the persistent challenge of processing the high volume and density of LiDAR data, which is crucial for precise terrain analysis.

Solar PV panel performance and sustainable deployment are intrinsically linked to accurate terrain assessment and optimal site selection. While LiDAR technology provides highly precise topographical information, the sheer volume of raw point cloud data often impedes efficient processing. The research team, led by Baofeng Wan and Kai Qin, tackled this by implementing an octree-based compression technique, achieving an impressive 80% compression rate, thereby streamlining data handling without compromising critical detail. This compressed data was then used to generate high-resolution Digital Terrain Models (DTMs) through advanced interpolation methods.

The methodology involved evaluating four interpolation techniques—IDW, RBF, TPS, and EBK—with the Empirical Bayesian Kriging (EBK) method ultimately selected for its superior DTM generation from the compressed datasets. Following DTM creation, a multi-factor analysis was performed, integrating critical environmental variables: slope (S), aspect (AS), and maximum solar radiation (SR). This comprehensive analysis enabled the precise identification of optimal PV panel installation areas.

In three distinct test zones, the framework successfully delineated significant optimal installation areas, measuring 82,360 square meters, 302,462 square meters, and 97,464 square meters, respectively. These results underscore the framework's capability to provide robust scientific support for PV site selection, particularly in complex or mountainous regions where traditional surveying methods are less efficient or cost-prohibitive. The integration of point cloud compression with multi-factor geospatial analysis represents a substantial leap forward in addressing the challenges of large-scale terrain data processing for renewable energy infrastructure.

The proposed method offers a new technological reference for solar energy development, promising to enhance project viability and accelerate deployment in diverse geographical settings. While the study marks a significant advancement, the researchers acknowledge ongoing challenges in constructing even more precise models from LiDAR data. Future research will concentrate on further improving data processing techniques to ensure even greater accuracy and efficiency in PV panel placement, paving the way for more resilient and higher-performing solar installations worldwide.