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The surge in demand for sustainable transportation has accelerated the adoption of electric vehicles (EVs). Despite their benefits, EVs face challenges such as limited driving range and frequent recharging needs. Addressing these issues, innovative energy optimization techniques have emerged, prominently featuring machine learning-driven solutions. This paper reviews work in the areas of Smart EV energy optimization systems that leverage machine learning to analyse historical driving data. By understanding driving patterns, road conditions, weather, and traffic, these systems can predict and optimize EV energy consumption, thereby minimizing waste and extending driving range. Concurrently, renewable microgrids present a promising avenue for bolstering power system security, reliability, and operation. Incorporating diverse renewable sources, these microgrids play a pivotal role in curbing greenhouse gas emissions and enhancing efficiency. The review also delves into machine learning-based energy management in renewable microgrids with a focus on reconfigurable structures. Advanced techniques, such as support vector machines, are employed to model and estimate the charging demand of hybrid electric vehicles (HEVs). Through strategic charging scenarios and innovative optimization methods, these approaches demonstrate significant improvements in microgrid operation costs and charging demand prediction accuracy.

This paper offers a comprehensive review of the advancements in the realm of renewable energy, specifically focusing on solid oxide fuel cells and electrolysers for green hydrogen production. The review delves into the significance of wind energy as a pivotal renewable energy source and underscores the importance of precise forecasting for efficient energy management and distribution. The integration of machine learning-based approaches, such as Support Vector Regression and Random Forest Regression, has shown promising results in enhancing the accuracy of wind energy production forecasts. Furthermore, the paper explores the broader landscape of renewable energy generation forecasting, emphasizing the rising prominence of machine learning and deep learning techniques. As the penetration of renewable energy sources into the electricity grid intensifies, the need for accurate forecasting becomes paramount. Traditional methods, while valuable, have encountered limitations, paving the way for advanced algorithms capable of deciphering intricate data relationships. The review also touches upon the inherent challenges and prospective research avenues in the domain, including addressing uncertainties in renewable energy generation, ensuring data availability, and enhancing model interpretability. The overarching goal remains the seamless integration of renewable sources into the grid, propelling us towards a greener future.