# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 5 Conclusions  
In this paper, a novel method was introduced for detecting individual photovoltaic (PV) modules by fusing remote sensing data from airborne Light Detection and Ranging (LiDAR) point cloud and Very High Resolution (VHR) satellite imagery. Before the proposed method was tested, several various semantic segmentation methods based on U-shaped architectures were trained on a newly annotated VHR dataset for the detection of 2D modules. The fully Transformer-based method FT-UnetFormer with the SwinTransformer encoder backbone provided the highest results in terms of IoU and F1 metrics, with the Fine Optimization Module (FOM) applied, yielding superior results. The proposed method was then tested on an airborne LiDAR scan and VHR imagery of the University of Maribor campus. The detected 2D modules were successfully reconstructed in 3D and grouped into 3D arrays. A state-of-the-art solar irradiance simulation method was considered, accounting for shadowing from the surroundings and anisotropic diffuse irradiance, in order to estimate the total per-annum received solar irradiance of each module and, consequently, the expected electricity production using the nominal efficiency of a given module. The simulated electricity production closely matched the electricity measurements of four PV systems at the campus, with an error of below 6.7 %. Finally, the proposed optimization using Simulated Annealing (SA) was used to estimate the potential increase in production if the tilt angle changed, where an increase of up to 4.9 % was estimated in the best case scenario. This research also marks the first effort to demonstrate that the fusion of remote sensing data, coupled with a deep learning model, solar irradiance simulation, and optimization, can estimate the optimal tilt angles for existing array installations automatically, which has not been performed before. Therefore, the authors anticipate that the proposed method is highly useful in dense urban environments with a high concentration of obstructions, where the optimization of tilt angles is more crucial due to increased shadowing. Moreover, the proposed method could be seamlessly integrated with standard PV monitoring infrastructures (e.g., SCADA [58] or other smart metering platforms) that track real-time power output, meteorological conditions, and fault status. By accessing these data streams, the method performance assessments could be calibrated or validated against on-site measurements, thus enhancing accuracy and reliability for tilt angle optimization. In turn, continuous monitoring could enable a feedback loop, ensuring that any tilt angle modifications translate into measurable gains. For future work, developing more advanced semantic segmentation models tailored for the detection of PV modules could yield improved accuracy, especially in challenging urban environments characterized by strong shadows, oblique viewing angles, or partial occlusions. Additionally, the proposed method could be extended to consider finding theoptimal aspect angle for a given PV array, under the constraints that rotation would be physically possible in case of obstructions or available surface area. Furthermore, migrating from a 2.5D topological grid to full 3D triangulated models of buildings could offer more precise solar irradiance and shadowing analyses. Moreover, extending the irradiance simulation to account for thermal, wind-load and structural constraints would bring the optimization closer to real-world conditions. Further refinements could involve embedding cost-benefit calculations, e.g., considering local feed-in tariffs, within the optimization loop. Even in the case that the method provides marginal improvements, a small enhancement can be significant for large-scale PV systems over an extended operational period.