| Title: | Detection and optimization of photovoltaic arrays’ tilt angles using remote sensing data |
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| Authors: | ID Lukač, Niko (Author)
ID Seme, Sebastijan (Author)
ID Sredenšek, Klemen (Author)
ID Štumberger, Gorazd (Author)
ID Mongus, Domen (Author)
ID Žalik, Borut (Author)
ID Bizjak, Marko (Author) |
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| Files: |  applsci-15-03598-v2.pdf (11,60 MB)
MD5: 1B717F49A769548CB79EE8AADC4025F5
 https://www.mdpi.com/2076-3417/15/7/3598
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
FE - Faculty of Energy Technology
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| Abstract: | 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%. |
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| Keywords: | solar energy, photovoltaics, semantic segmentation, optimization, LiDAR, VHR imagery |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 12.02.2025 |
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| Article acceptance date: | 24.03.2025 |
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| Publication date: | 25.03.2025 |
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| Publisher: | MDPI AG |
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| Year of publishing: | 2025 |
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| Number of pages: | 22 str. |
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| Numbering: | let. 15, št. 7, št. članka 3598 |
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| PID: | 20.500.12556/DKUM-93491  |
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| UDC: | 621.383.51 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 230279939 |
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| DOI: | 10.3390/app15073598 |
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| Copyright: | © 2025 by the authors |
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| Publication date in DKUM: | 22.07.2025 |
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| Views: | 156 |
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| Downloads: | 13 |
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| Metadata: |    |
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| Categories: | Misc.
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