AI-Driven Renewable Energy Forecasting and Grid Optimization

Abstract

The intermittent nature of renewable energy sources such as solar and wind presents challenges for grid stability and energy planning. This paper examines machine learning applications in renewable energy forecasting, integrating AI-driven predictive models to enhance grid efficiency. Techniques such as recurrent neural networks (RNNs), support vector machines (SVMs), and reinforcement learning are employed to predict energy generation, optimize load balancing, and reduce reliance on fossil fuels. Case studies highlight real-world implementations where AI enhances energy sustainability by improving renewable energy integration into the power grid.