20.500.12556/DKUM-93491 Detection and optimization of photovoltaic arrays’ tilt angles using remote sensing data Maximizing the energy output of photovoltaic (PV) systems is becoming increasingly important. Consequently, numerous approaches have been developed over the past few years that utilize remote sensing data to predict or map solar potential. However, they primarily address hypothetical scenarios, and few focus on improving existing installations. This paper presents a novel method for optimizing the tilt angles of existing PV arrays by integrating Very High Resolution (VHR) satellite imagery and airborne Light Detection and Ranging (LiDAR) data. At first, semantic segmentation of VHR imagery using a deep learning model is performed in order to detect PV modules. The segmentation is refined using a Fine Optimization Module (FOM). LiDAR data are used to construct a 2.5D grid to estimate the modules’ tilt (inclination) and aspect (orientation) angles. The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. The method was validated using PV systems in Maribor, Slovenia, achieving a 0.952 F1-score for module detection (using FT-UnetFormer with SwinTransformer backbone) and an estimated electricity production error of below 6.7%. Optimization results showed potential energy gains of up to 4.9%. solar energy photovoltaics semantic segmentation optimization LiDAR VHR imagery sončna energija fotovoltaika pomenska segmentacija optimizacija LiDAR VHR posnetki true false true MDPI AG Angleški jezik Slovenski jezik https://www.mdpi.com/journal/applsci © 2025 by the authors Članek v reviji 2025-06-28 03:08:47 2025-07-22 08:39:20 2025-07-23 03:11:06 0000-00-00 00:00:00 2025 0 0 22 str. št. 7, št. članka 3598 let. 15 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-02-12 2025-03-24 2025-03-25 621.383.51 230279939 10.3390/app15073598 2076-3417 applsci-15-03598-v2.pdf applsci-15-03598-v2.pdf 1 1B717F49A769548CB79EE8AADC4025F5 7bb90a263043a84069fad5f7f7a8212186147f6fdb8cae14a6337ee7de30b28f cb5846ff-66c6-11f0-b4a7-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=193892 https://www.mdpi.com/2076-3417/15/7/3598 1 71ee3279-53bc-11f0-a27c-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=192875 Fakulteta za elektrotehniko, računalništvo in informatiko Fakulteta za energetiko 0 0 0