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A GIS and Gen-AI-Driven framework for automated renewable energy resource assessment and infrastructure optimization
Independent Researcher, India.
Review
International Journal of Science and Research Archive, 2024, 13(02), 4109-4118.
Article DOI: 10.30574/ijsra.2024.13.2.2370
Publication history:
Received on 23 October 2024; revised on 14 December 2024; accepted on 16 December 2024
Article DOI: https://doi.org/10.30574/ijsra.2024.13.2.2370
Abstract:
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.
Keywords:
Geographic Information System; Generative AI; Renewable Energy Resources; GAN; Hydropower; Smart Grid
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
