Numerical Analysis and Presentation of the Interest of Implementing Artificial Intelligence in a Green Hydrogen Generator Set Using Energy Efficiency Techniques

The significant increase in the usage of renewable energy sources has sparked a movement to combine them with the generation of green hydrogen in order to address the intermittency issues that cause either a surplus or a deficit in energy production. The integration of green hydrogen in the energy sector is taking place on a large, medium, and small scale, from replacing natural gas with green hydrogen in thermal power plants to creating green hydrogen domestic batteries. The significance of incorporating AI into green hydrogen production is examined in this paper using a hydrogen power generator that has been the focus of earlier research. The function and influence of AI in the hydrogen energy generator are discussed, demonstrating how artificial neural networks (ANNs) enable the generator to forecast hydrogen production by predicting changes in solar irradiation. Additionally, it demonstrates how AI could assure the safety of the hydrogen fluidic circuit and manage the command control of various components by prioritizing storage and direct supply or supply through storage while enabling specific prevention maintenance. The numerical analysis results obtained in this study show that a generator integrating AI for a defined consumption level enables an optimized choice of components, a controlled level of safety, a longer service life, and have a lower return on investment than a hydrogen generator operating without AI. This study shows that integrating AI into energy systems using green hydrogen as an energy medium is more cost-effective from.