Abstract: [Objectives] As an essential sustainable energy technology, renewable energy-based water electrolysis hydrogen production has gained widespread attention due to its environmental protection and low carbon emissions advantages. However, traditional water electrolysis hydrogen production technology faces challenges in terms of efficiency and cost. This paper aims to explore key applications of artificial intelligence (AI) in optimizing the efficiency and economic performance of water electrolysis hydrogen production systems and its development prospects. [Methods] Common AI tools such as MATLAB, Python, and SimuNPS are utilized for algorithm development, deep learning model training, and multi-physics simulation in water electrolysis hydrogen production systems. By incorporating AI technology to achieve power output prediction, system capacity optimization and scheduling, and fault diagnosis, system performance and stability are enhanced. [Results] Existing experimental results indicate that AI technology has achieved significant improvements in system efficiency and economic performance, effectively enhancing the overall performance of water electrolysis hydrogen production systems and demonstrating good stability in different application scenarios. [Conclusions] AI technology presents new opportunities for the development of renewable energy-based water electrolysis hydrogen production. Despite ongoing technical and economic challenges, the growth in policy support and market demand will drive further development in this field. This paper provides important directions and insights for future related research.

Key words: renewable energy, electrolysis for hydrogen production, AI