Abstract
There is consented efforts globally to transition to green energy as a way to combat global 9 warming. This study investigates the application of Adaptive Artificial Evolution (AAE) techniques 10 to enhance the production of green hydrogen, focusing on optimizing electrolyzer efficiency, re‐ 11 newable energy inputs, and minimizing energy losses. Green hydrogen, produced through the elec‐ 12 trolysis of water using renewable energy sources, presents a sustainable alternative to fossil fuels, 13 with significant potential for reducing carbon emissions and supporting energy transition goals. 14 The integration of AAE can facilitate the optimization of electrolyzers, which are critical for efficient 15 hydrogen production, by adapting their operational parameters in real‐time based on fluctuating 16 renewable energy inputs such as solar and wind. Moreover, the scalability and cost‐effectiveness of 17 green hydrogen production are paramount for its widespread adoption. Recent advancements in 18 electrolyzer technology and the coupling of renewable energy sources directly to electrolyzers can 19 significantly reduce production costs and improve overall efficiency. AAE techniques can be em‐ 20 ployed to dynamically adjust the operational strategies of these systems, thereby enhancing their 21 performance under varying conditions and contributing to a more resilient energy system. This re‐ 22 search underscores the importance of innovative approaches like AAE in addressing the challenges 23 of green hydrogen production, ultimately supporting the transition to a sustainable energy future. 24 The key findings and global models presented in this study are recommended in helping Africa 25 countries accelerate their green hydrogen drive. 26