Enhancing Renewable Energy Integration Through Machine Learning-Based Forecasting
Abstract
The growing adoption of renewable energy sources such as solar and wind presents challenges in grid stability and energy distribution due to their intermittent nature. This paper explores machine learning techniques for improving renewable energy forecasting, optimizing grid operations, and enhancing energy storage management. Time series analysis, deep learning models, and reinforcement learning are applied to predict energy production patterns and balance supply-demand dynamics. Case studies on AI-powered smart grids demonstrate how machine learning enhances renewable energy integration, reduces reliance on fossil fuels, and promotes a sustainable energy transition.
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