(99b) Harnessing AI and ML for Green Hydrogen: From Production Prediction to Global Potential

This presentation summarizes two case studies exploring innovative applications of Artificial Intelligence (AI) and Machine Learning (ML) in decarbonization, more specifically green hydrogen production, focusing on prediction and global potential assessment. The first study introduces a novel Spatiotemporal Attention Framework (STAF) for predicting hydrogen production capacity from solar power plants. This dual-pathway architecture, combining CNN and attention BiGRU, improves prediction accuracy by 18.46% compared to hybrid deep learning models. The study also incorporates an economic analysis using the levelized cost of hydrogen (LCOH) to evaluate production viability, offering diverse scenarios for strategic planning. The second study analyzes green hydrogen production potential across global climatic zones, integrating technical feasibility and economic viability analyses. It employs an attention-based deep learning model to predict and assess the financial resilience of green hydrogen projects. The study reveals significant variations in production capacity and risk profiles across different regions, with desert and arid areas favoring solar-based production and coastal regions preferring wind-based production. Balanced resource utilization can reduce uncertainty by 15-25%, while hybrid electrolyzer configurations demonstrate 20% better capacity utilization. Both studies highlight the transformative potential of AI/ML in advancing green hydrogen production, offering crucial strategies for optimizing processes, reducing costs, and improving integration with renewable energy sources.