A GIS and Gen-AI-Driven framework for automated renewable energy resource assessment and infrastructure optimization

As the globe moves towards renewable energy, maximizing the utilization of renewable resources and enhancing infrastructure management are important issues that conventional approaches find difficult to handle successfully. This paper presents a novel approach that integrates Geographic Information Systems (GIS) with Generative Artificial Intelligence (Gen-AI) to improve an efficiency of renewable energy systems. Moreover, GIS has strong instruments for analyzing issues concerning geography, whereas Gen-AI has enhanced features for energy rate prediction, selection of areas that are suitable for energy production, and control of facilities. These technologies have been proposed to work in synergy to address some of the complex issues like determination of exact locations of the sites, estimating energy generation at any time and controlling real-time energy requirements. Furthermore, it is applied to predict and improve maintenance as well as correct configuration of smart grids to become more effective and eco-friendly energy distributing system. The applications of the framework include many REN sectors, such as solar, wind, hydro, and bioenergy, solving the issues of rearing losses, environmental effects, and varying energy demands. Through solving these urgent problems, integrating GIS and Gen-AI constructs the groundwork for a more sustainable, robust and future energy structure.