Next-generation control for electrolyzers: a review of GPT-based AI frameworks in renewable hydrogen systems

Hydrogen is increasingly recognized as a cornerstone of the clean energy transition, with renewable-powered electrolyzers enabling carbon-neutral hydrogen production. However, integrating variable renewable energy sources with electrolyzers remains technically challenging due to intermittency, dynamic load requirements, and efficiency losses. While conventional artificial intelligence (AI) techniques such as fuzzy logic, neural networks, and reinforcement learning have shown promise in optimization and control, they remain fragmented, data-intensive, and limited in scalability. This review introduces a novel perspective by providing the first dedicated analysis of Power Electronics–Generative Pre-trained Transformers (PE-GPT) for renewable hydrogen systems. Unlike existing surveys that address hydrogen technologies or AI applications in isolation, this paper systematically bridges both domains, demonstrating how domain-specific generative AI frameworks can optimize converter design, real-time modulation, and system-level energy management. The review further outlines hybrid architectures that integrate AI across power systems and power electronics, creating a unified pathway toward intelligent, reliable, and cost-effective hydrogen production. By synthesizing technical advances and highlighting open challenges, this work establishes a research roadmap that positions AI-driven frameworks as a transformative enabler for scalable green hydrogen deployment. To demonstrate practical feasibility, MATLAB/Simulink-based simulation results comparing a conventional PI-controller with a PE-GPT-assisted controller for a DC–DC converter feeding an electrolyzer are included, confirming the performance advantages of the proposed approach.