Machine learning from renewables to green hydrogen production optimization: A PM-CFL-OPT framework addressing uncertainty, safety constraints, and interpretability

This study critically synthesizes machine learning for renewable-to-green hydrogen production optimization and introduces a PM-CFL-OPT architecture that links predictive modelling, clustering and feature learning, and optimization and control into a single data-to-decision framework. The review shows that the strongest technical gains arise when forecasting accuracy, mechanistic realism, and operational control are treated jointly rather than as isolated modelling tasks. In solar-driven hydrogen contexts, hybrid decomposition-boosting pipelines markedly outperform standalone learners, with CEEMD-SCM-GA-LGBM achieving an average R² of 0.99, compared with 0.87 for MLP, while reducing RMSE from 94.86 to 31.17 and MAE from 57.82 to 24.03. Physics-informed learning also shows clear deployment value: for electrified steam methane reforming, a physics-informed neural network improves hydrogen-prediction R² from 0.93 to 0.99, lowers hydrogen RMSE by 15.4%, reduces hydrogen MAE by 20.0%, and cuts CO₂ RMSE by 33.3% relative to a conventional deep neural network. In data-informed hybrid system design, nonlinear kernel and sequence-sensitive models outperform simpler baselines, with SVR exceeding linear regression by ΔR² = 0.144 and lowering MSE by 7.606. These quantitative patterns indicate that the most credible pathway for hydrogen optimization is not algorithmic complexity alone but the integration of signal conditioning, uncertainty-aware learning, physics-constrained modelling, and control-oriented intelligence. The principal conclusion is that machine learning is now mature enough to support forecasting, surrogate modelling, flexible dispatch, and techno-economic design, yet deployment readiness remains limited where safety, degradation, and uncertainty propagation are weakly embedded.