A Review on Machine Learning Applications in Hydrogen Energy Systems

Adopting machine learning (ML) in hydrogen systems is a promising approach that enhances the efficiency, reliability, and sustainability of hydrogen power systems and revolutionizes the hydrogen energy sector to optimize energy usage/management and promote sustainability. This study explores hydrogen energy systems, including production, storage, and applications, while establishing a connection between machine learning solutions and the challenges these systems face. The paper provides an in-depth review of the literature, examining not only ML techniques but also optimization algorithms, evaluation methods, explainability techniques, and emerging technologies. By addressing these aspects, we highlight the key factors of new technologies and their potential benefits across the three stages of the hydrogen value chain. We also present the advantages and limitations of applying ML models in this field, offering recommendations for their optimal use. This comprehensive and precise work serves as the most current and complete examination of ML applications within the hydrogen value chain, providing a solid foundation for future research across all stages of the hydrogen industry.