AI-optimized renewable energy forecasting for U.S. power grids

Renewable energy has emerged as a critical component in the global pursuit of sustainable development and carbon neutrality. Despite its potential, the inherent challenges associated with renewable energy sources, such as intermittency, variability, and storage limitations, necessitate innovative solutions to enhance efficiency and reliability. 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 and combining traditional time-series methods and machine learning model tools, we conclude that the use of AI facilitates a speedy generation of better forecasting and dependency on renewable energy resources. As the demand for energy in the world continues to grow, the integration of renewable energy into smart grids has become essential for building efficient and sustainable power grid networks. In the study, artificial intelligence has become a transformative tool. Using machine learning techniques along with traditional techniques has promoted greater confidence and dependency on renewable energy resources. 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 role of AI in enhancing global sustainable goals for energy systems. This research also contributes to policymaking by evaluating AI’s potential in shaping sustainable energy strategies, ensuring a reliable transition to clean energy.