# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 1 Introduction
They also fixed the tilt angle on flat roofs at 32\({}^{\circ}\) for their specific location, as only the aspect angle, not the tilt angle, can be deduced from the VHR images alone [30]. Similarly, [PERSON] et al. [33] estimated the PV capacity by counting the panels detected by U-Net and estimated the packing density parameter. More recently, [PERSON] et al. [34] approximated the energy output of identified PV systems considering the expected global irradiance at a tilt angle based on the location's longitude. Figure 1: Example VHR imagery of two PV systems, where (**a**) is the raw color image, where various methods can detect either (**b**) PV systems (low granularity), (**c**) individual PV arrays (medium granularity), or (**d**) individual PV modules (high granularity). The colors represent individual instances of detected PV arrays or modules. In this paper, the challenge of optimizing the tilt angles of existing PV arrays' installation by fusing airborne 3D LiDAR point cloud data and VHR imagery data is addressed. To the best of the authors' knowledge, this is the first work to tackle this challenge using widely available remote sensing data sources. The proposed method not only improves the accuracy of the estimated production and capacity of PV-based electrical energy for existing installations, but also enables the optimization of the tilt angles using a simulation-driven method. This also helps to determine whether existing installations already employ optimal angles or if there is significant room for improvement. For fusing VHR with 3D airborne LiDAR, the detection of 2D individual PV modules in VHR data by using a semantic segmentation model is utilized. Then, a topological structure over the LiDAR point cloud is established, which enables the estimation of each PV module's slope (inclination) and aspect (orientation) angles, and allows subsequent 3D reconstruction after the consideration of 2D PV module detection from VHR. Finally, to find the optimal tilt angles of the 3D PV modules grouped into 3D PV arrays, an optimization algorithm coupled with irradiance simulation is utilized for each module in the optimization iteration. Thus, the primary novelties over related work are as follows:  
* A new method is introduced that automatically fuses data from airborne LiDAR and VHR imagery in order to estimate individual modules' 3D structure, as well as their tilt (inclination) and aspect (orientation) angles. The prior state-of-the-art relied on a single data source and identified entire arrays or PV systems, often overlooking the tilt angle estimation due to a shortage of 3D information. * The 3D-reconstructed modules are spatially positioned within a topological equidistant grid constructed from LiDAR data. This positioning allows for the further simulation of the location-specific solar irradiance for the reconstructed 3D modules, by taking into account shadowing from the surrounding environment and anisotropic diffuse irradiance based on the Perez model. * Simulated Annealing (SA) is used for optimizing the tilt angles on the reconstructed 3D arrays, which consist of grouped 3D modules based on their proximity, tilt, and aspect. During the optimization cycle, a per-annum solar irradiance simulation is run for each module, where a hypothetical tilt angle change occurs to determine the optimal tilt angle. Although optimization of hypothetical PV systems has been performed before, optimization of existing installations by considering a high-resolution granularity by using fused remote sensing data has not been performed before. * By further considering the specifications of the PV system, the estimated annual solar irradiance for the detected arrays can be used to predict the potential increase in annual PV electrical energy production (i.e., the PV potential) after tilt angle optimization. The results demonstrate an example of such analysis. This paper is organized into five sections.