# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Encoder** & **Parameters** & **Complexity** & **\(-\) FOM** & **+ FOM** & \\  & **Backbone** & **[M]** & **[G]** & **F1** & **IoU** & **F1** & **IoU** \\ \hline BAnet ([PERSON] et al., 2020) [35] & ResNet-18 [54] & 12.7 & 58.9 & 0.927 & 0.864 & 0.937 & 0.882 \\ A2-FPN ([PERSON] et al., 2021) [36] & ResNet-18 [54] & 12.2 & 52.8 & 0.898 & 0.815 & 0.934 & 0.876 \\ MAnet ([PERSON] et al., 2022) [38] & ResNet-18 [54] & 12.0 & 89.5 & 0.922 & 0.855 & 0.941 & 0.889 \\ MAnet ([PERSON] et al., 2022) [38] & EfficientNet-b5 [22] & 28.0 & 84.0 & 0.942 & 0.890 & 0.949 & 0.904 \\ MAnet ([PERSON] et al., 2022) [38] & SE-ResNeXt50 [23] & 27.1 & 314.3 & 0.947 & 0.900 & 0.948 & 0.901 \\ DCSwin ([PERSON] et al., 2022) [37] & SwinTransformer [55] & 66.9 & 285.5 & 0.899 & 0.817 & 0.935 & 0.878 \\ UnetFormer ([PERSON] et al., 2022) [39] & ResNet-18 [54] & 11.7 & 47.6 & 0.905 & 0.827 & 0.939 & 0.886 \\ UnetFormer ([PERSON] et al., 2022) [39] & EfficientNet-b5 [22] & 27.8 & 58.2 & 0.914 & 0.842 & 0.948 & 0.901 \\ UnetFormer ([PERSON] et al., 2022) [39] & SE-ResNeXt50 [23] & 15.5 & 68.0 & 0.911 & 0.837 & 0.942 & 0.891 \\ FT-UnetFormer ([PERSON] et al., 2022) [39] & SwinTransformer [55] & 96.0 & 512.5 & 0.915 & 0.843 & **0.952** & **0.908** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Averaged inference results of various deep learning methods for individual 2D modules’ detection after 5-fold cross validation run on the test dataset. The highest result is styled in bold. Ablation analysis is shown when performing detection with or without FOM ([PERSON] et al., 2023) [21]. The validation of the proposed method was carried out on PV systems installed at the University of Maribor campus's buildings in Slovenia, geolocated at 46\({}^{\circ}\) 33\({}^{\prime}\) 48.77\({}^{\prime\prime}\) N, 15\({}^{\circ}\) 37\({}^{\prime}\) 30.68\({}^{\prime\prime}\) E. Figure 7a shows a classified 2.5D grid from airborne LiDAR data for the given campus location with a resolution of \(res_{G}=0.5\) m\({}^{2}\), where the coloring is based on three classes of the underlying point cloud data, namely, buildings, terrain, and high vegetation. Figure 7b shows the coloring based on estimated normal vectors for each cell in the grid (used to derive slope and aspect angles), while Figure 7c-f show an example result of using the considered solar irradiance simulation method for different components, namely, average shadowing (Figure 7c), direct irradiance (Figure 7d), anisotropic diffuse irradiance (Figure 7e), and total solar irradiance--the solar potential (Figure 7f). By using the 2 nd and 4 th rows, the 2 nd and 4 th rows show the difference between the ground truth masks of detected modules and the results of detection using an FT-UnetFormer model with corresponding IoU and F1 metrics per image. The red and blue colors indicate overprediction and underprediction, respectively. Figure 5: Inference results on sample images from the testing dataset, where the 1 st and 3 rd rows show the original satellite imagery, while the 2 nd and 4 th rows show the difference between the ground truth masks of detected modules and the results of detection using an FT-UnetFormer model with corresponding IoU and F1 metrics per image. The red and blue colors indicate overprediction and underprediction, respectively. Figure 6: Results of the FT-UnetFormer with SwinTransformer model regarding per-epoch change of (**a**) F1 score and (**b**) learning loss for training and validation data, respectively. the proposed method, this simulation is repeatedly re-run within the SA optimization algorithm in each iteration when the tilt angle is adjusted for each PVA. The LiDAR data, as well as long-term direct and diffuse horizontal meteorological measurements, in order to construct TMY, were provided by the Slovenian Environment Agency. The meteorological data were sourced from a national meteorological station located 5 km away at the Edvard Rusjan International Airport.