AI-ENABLED OPTIMIZATION OF SOLAR PHOTOVOLTAIC–HYDROGEN ELECTROLYSER SYSTEMS FOR SUSTAINABLE ENERGY TRANSITION

Green hydrogen that is produced through the process of electrolysis with renewable energy is already considered one of the linchpins of the global energy switchover. Even with a rapid technological development, operational inefficiencies as well as intermittency of renewable supply and safety issues still continue to hinder large scale deployment. The paper under study explores how artificial intelligence (AI) and machine learning (ML) can be utilized to transform solar photovoltaic (PV)-hydrogen systems into sustainable energy integration. We also provide a comparative analysis of AI-based algorithms such as supervised learning models, random forests, genetic algorithms, reinforcement learning, and fuzzy logic controllers with their corresponding strengths in predicting the availability of renewable energy, improving the efficiency of electrolyser, predicting maintenance requirements, and allowing dynamic grid integration. In addition to technical optimization, we touch on important regulatory, ethical, and safety issues related to AI-based hydrogen production, including data governance, transparency, and operational risks. By summarizing the findings of recent studies and defining the current methods deficiencies, this paper suggests a model to apply AI to hydrogen systems, which will be efficient, safe, and sustainable. The results emphasize opportunities of AI to expedite the process of green hydrogen infrastructure deployment and help in building resilient low-carbon energy networks.