From Data to Power: AI-Enhanced Renewable Energy Systems for the Smart Grid Era

This work aims at analyzing the part of AI given its capability to improve reliability, efficiency, and flexibility of renewable energy integration. The growing world demand for energy requires the incorporation of renewable energy into smart grids to create effective and efficient power systems. Through the utilization of sophisticated machine learning, predictive analytical tools, and real-time calculated data, we implemented forecasts, schedules, and management of renewable power within the grid. Our methods included establishing models that predict renewable power generation, demand, and real-time operations of the grid. According to the findings, the power oscillation range has been decreased by 30%, the use of renewable energy generation has been increased by 25%, and the dependence of fossil fuel backup generation has been decreased by 40% based on the usage of AI-enabled systems. Moreover, several of the AI-fortified systems were much more capable of maintaining the stability of the grid, cutting energy costs and CO2 emissions by 20 on average. These insights demonstrate AI’s capability to facilitate smarter and more antifragile energy grids by navigating renewable energy and supply-demand Plexus effectively. Overall, our findings support the statement that AI-based renewable energy systems can help integrate the transition to more sustainable energy resources by enhancing grid performance, reducing carbon footprints, and improving energy access. This study also reveals the significant reality of AI to enhance global sustainable goals for energy systems.