Evaluating Solar Energy Potential through Geographic Information System and Remote Sensing: A Systematic Review

To optimize the potential of solar energy, it is essential to analyze the amount of solar energy that can be harnessed. In this article, we provide a comprehensive review of the literature on the use of remote sensing (RS) and Geographic Information System (GIS) for assessment and maintenance of solar energy systems. The study reveals that researchers have employed LiDAR and unmanned aerial systems (UAS) to identify and monitor photovoltaic installations. ArcGIS solar and Quantum GIS (QGIS) packages are utilized and found suitable to optimize PV plant operation and identify suitable locations for photovoltaic power plants in case studies. Moreover, in some recent articles, machine learning and deep learning algorithms have been exploited for optimizing the performance and installation of solar energy systems. It can be concluded that RS, particularly through satellite imagery, enables the analysis of long-term trends in solar energy availability and aids in identifying optimal locations for solar installations. RS proves valuable in assessing the productivity and efficiency of solar energy systems, considering factors like temperature, wind speed, and humidity, allowing for system performance optimization. The incorporation of a combination of satellite and UAV data into machine learning algorithms can play a vital role in acquiring accurate data.