Assessing Photo-Voltaic Potential in Urban Environments: A Comparative Study between Aerial Photogrammetry and LiDAR Technologies

Abstract

Urban development and population growth have significantly increased energy demands, predominantly met by non-renewable resources, negatively impacting nature and climate. Consequently, there has been a global shift towards renewable energy sources, with solar energy being widely adopted. Photovoltaic (PV) potential measures the usable electricity from solar energy using PV technology. With conventional methods, estimating PV potential involves converting 3D point cloud data to 2.5D elevation models, which can affect the accuracy of the estimation. A research gap exists in using adequate 3D point cloud datasets for PV potential estimation considering roof surface area and the surface normal vectors. This study compares aerial photogrammetry and aerial LiDAR point clouds for PV potential estimation against the DSM-based approach. Accurate PV potential estimation must consider solar incidence, roof area, azimuth, tilt angles, and PV efficiency. Traditional 2.5D methods often overlook crucial azimuth and tilt data, limiting the accuracy of the PV estimation. Converting 3D data to 2.5D may result in information loss, while 3D analyses offer higher accuracy. To investigate the mentioned gaps, this research aims to evaluate the capabilities of photogrammetry and LiDAR for urban PV potential estimation, highlighting their feasibility and accuracy over 2.5D methods.