Research on solar photovoltaic panel layout based on compressed LiDAR point cloud

Solar photovoltaic (PV) panels convert solar energy into clean, renewable electricity. Their efficiency and sustainable application are highly dependent on precise terrain analysis and optimal site selection. While LiDAR technology enables the acquisition of dense point cloud data for accurate terrain modeling, its high data volume poses significant challenges for efficient processing. This study proposes a workflow to generate high-resolution digital terrain models (DTMs) by combining octree-based point cloud compression (achieving an 80% compression ratio via elevation zoning) with four interpolation methods: inverse distance weighted (IDW), radial basis function (RBF), thin plate spline (TPS), and empirical Bayesian kriging (EBK). Multi-factor analysis is then used to identify optimal PV panel installation areas by integrating key terrain factors—slope (S), aspect (AS), and maximum solar radiation (SR).The proposed approach was validated in three test zones (A, B, and C) in Dongchuan District, northeastern Kunming, Yunnan Province, China. The optimal installation areas identified in these zones were 82,360 m 2 , 302,462 m 2 , and 97,464 m 2 , respectively. The EBK-generated DTM was selected as the most accurate for terrain analysis. The workflow significantly reduced data volume and processing time while maintaining high DTM accuracy (error range: 0.004–0.008 m compared to uncompressed data). This study demonstrates that efficient point cloud compression and multi-factor analysis can address the challenges of large-scale terrain data processing and site optimization for PV deployment in complex terrains. The proposed method offers new technological guidance for PV site selection. Future research will focus on further improving data processing techniques to enhance DTM precision and PV panel placement accuracy in more diverse geographic contexts.