Machine Learning Applications in Hydrogen Energy Systems: Optimization and Predictive Analytics

Abstract

More environmentally friendly energy options are currently required to replace conventional power generating resources like fossil fuels due to global demands, especially in developed and rising countries. Fossil fuel-based energy sources are responsible for two detrimental environmental issues: changing the climate and global warm ing. According to the International Renewable Energy Agency (IRENA), more than 64% of the new renewable power capacity in 2024 came from China. Future clean fuels might include hydrogen energy, however overcoming these obstacles will need infrastructure expansion, cost reduction, supporting legislation, and technological im provements. This paper's goal is to comprehend and investigate hydrogen energy. In order to enhance the overall effectiveness of hydrogen energy in producing power, it has also been researched and contrasted. In this work, we present a study on the performance analysis and optimization of hydrogen fuel cell systems using machine learning (ML) methodologies. Through a comparative analysis of different hydrogen production methods, we have under stood the relationship between these processes and fuel cell efficiency as well as sustainability. The paper also studies time based efficiency analysis of fuel cell and system performance as influenced by AC output power for different cases. ML models can analyze operational data to detect patterns, forecast performance trends, and opti mize energy management strategies. The findings illustrate that the proposed data-driven modeling is incredibly useful to support an efficiency performance optimization approach, reliability assessment and adaptive control approaches for hydrogen fuel cell energy systems leading to further success to develop intelligent and sustainable hydrogen-oriented power generation. Analyzing the behaviour and performance of hydrogen fuel cell devices is extremely crucial, particularly when they are applied to practical energy applications like grid integration or trans portation.