Renewable energy, such as solar, wind, and hydroelectric power, play a crucial role in mitigating climate change and ensuring a sustainable future. However, the intermittent nature of these resources poses significant challenges in maintaining a reliable and resilient energy supply. Fluctuations in weather patterns, grid integration issues, and the need for energy storage solutions can cause instabilities and disruptions in the energy system. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges, offering innovative solutions for optimizing renewable energy generation, distribution, and management.

The overarching goal of this Research Topic is to explore the potential of AI in enhancing the resilience of renewable energy systems. Recent advances in machine learning, deep learning, and optimization algorithms have opened new avenues for improving the efficiency, reliability, and resilience of renewable energy infrastructure.

By leveraging AI techniques, researchers can develop predictive models for forecasting renewable energy generation, enabling proactive planning and resource allocation. Additionally, AI can optimize the integration of renewable energy technology into existing grids, facilitating seamless load balancing and minimizing energy losses. Further, AI-driven energy management systems can intelligently control energy storage systems, ensuring a consistent supply during periods of low renewable energy generation.

This Research Topic aims to showcase cutting-edge research and innovative applications of AI in the domain of renewable energy resilience. We will accept the following article types: Original Research, Review, and Mini Review. We invite contributions from researchers, industry professionals, and practitioners exploring the following themes:
• AI-powered forecasting models for renewable energy generation.
• Optimization techniques for grid integration and load balancing of renewable energy technology.
• AI-driven energy management systems for distributed renewable energy and storage.
• Machine learning algorithms for predictive maintenance and fault detection in renewable energy infrastructure.
• AI-enabled smart grids and microgrids for efficient energy management and resilience.
• Case studies and real-world applications of AI in enhancing renewable energy resilience.

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Frontiers in Energy Research

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