Treffer: Integrating machine learning and IoT in hydrogen production, storage, and distribution for a decarbonized transport future.

This study integrates reinforcement learning (RL) optimization and internet of things (IoT) monitoring within a MATLAB/Simulink simulation framework for hydrogen infrastructure. IoT sensors provide real-time data, enabling dynamic adjustments, while RL optimizes hydrogen logistics, reducing costs and emissions. This approach enhances predictive accuracy beyond conventional models, offering a scalable solution for sustainability. IoT sensors improve model precision, identifying underground storage as the most economical. Renewable energy integration lowered emissions by 97.8% (from 9.00 to 0.20 kg CO2-eq/kg H₂) and reduced hydrogen costs by 40% (from US$5.50 to US$3.30/kg), while RL optimization achieved US$15 000 in cost savings and a 30% emissions reduction. [ABSTRACT FROM AUTHOR]

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