Assessment of the Solar Potential of Buildings Based on Photogrammetric Data

Paulina Jaczewska (), Hubert Sybilski and Marlena Tywonek
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Paulina Jaczewska: Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland
Hubert Sybilski: Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland
Marlena Tywonek: Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland

Energies, 2025, vol. 18, issue 4, 1-35

Abstract: In recent years, a growing demand for alternative energy sources, including solar energy, has been observed. This article presents a methodology for assessing the solar potential of buildings using images from Unmanned Aerial Vehicles (UAVs) and point clouds from airborne LIDAR. The proposed method includes the following stages: DSM generation, extraction of building footprints, determination of roof parameters, map solar energy generation, removing of the areas that are not suitable for the installation solar systems, calculation of power per each building, conversion of solar irradiance into energy, and mapping the potential for solar power generation. This paper describes also the Detecting Photovoltaic Panels algorithm with the use of deep learning techniques. The proposed algorithm enabled assessing the efficiency of photovoltaic panels and comparing the results of maps of the solar potential of buildings, as well as identifying the areas that require optimization. The results of the analysis, which had been conducted in the test areas in the village and on the campus of the university, confirmed the usefulness of the above proposed methods. The analysis provides that the UAV image data enable generation of solar potential maps with higher accuracy (MAE = 8.5 MWh) than LIDAR data (MAE = 10.5 MWh).

Keywords: photovoltaic systems; map of solar potential; detection of solar panels; UAV imagery; airborne laser scanning; GIS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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