Regime-Aware Temporal Convolutional–Gated Recurrent Forecasting of Hydrogen Production in Wave-Driven PEM Electrolysis

This research introduces RA-TCGN, a compact regime-aware temporal convolutional-gated recurrent model for multi-step hydrogen-production forecasting in wave-powered PEM electrolysis. It establishes a leakage-safe, experiment-level evaluation showing that one-step accuracy is misleading and demonstrates that operating-regime conditioning reduces pooled LOEO RMSE by 28.6%. External testing confirms improved forecasting and substantially lower extreme errors while revealing regime-specific deployment limitations.