Application of Deep Learning in Monitoring Environmental Risks in Renewable Energy Projects

The integration of renewable energy sources like solar and wind is vital for sustainable energy, but these systems carry environmental risks that require advanced monitoring. Traditional methods often fail in real-time assessment, prompting the need for more accurate predictive models. This literature review explores the role of deep learning (DL) in enhancing risk detection and energy management in renewable projects. It highlights recent advancements such as deep transfer learning, which improves solar radiation prediction accuracy up to 98.89%, and the LADRC-SAC algorithm, which stabilizes power grid frequency. These methods boost operational efficiency and risk mitigation. Despite their promise, DL models face challenges in interpretability, scalability, and ethical implications. Future research should focus on creating transparent, scalable DL frameworks for real-time monitoring across varied renewable systems. Overall, DL holds transformative potential in reducing environmental risks and supporting a resilient, sustainable energy infrastructure.