Cross-Renewable Generalization of PEM Electrolyzer Hydrogen-Flow Forecasting: Multi-Horizon Evaluation and Physics Anchoring

Forecasting renewable-powered proton-exchange-membrane (PEM) electrolysis across unfamiliar power trajectories requires evaluation at the renewable-source level rather than within familiar profile families. We evaluated experimentally recorded hydrogen flow from one nominal 1.25 MW PEM-electrolyzer platform under four renewable-source operating domains—Simulated Wind, Simulated Wave, Historical Solar Photovoltaic, and Simulated Tidal—using leave-one-renewable-source-out validation, a 60 s process history, and direct 1, 5, 10, and 30 s horizons. Ridge, HistGradientBoosting, and LightGBM transferred usefully at short horizons but degraded as the horizon increased; Wave had negative R2 for all three models at 30 s. Source balancing and worst-source selection produced only local gains, and model-disagreement error awareness weakened with horizon. For one prespecified five-epoch Conv1d masked-reconstruction design and linear probe, self-supervised embeddings underperformed handcrafted descriptors in source-macro forecasting, without establishing a general limitation of self-supervision. A current-command-based operational gray-box anchor (F1) outperformed its residual-augmented version (G1) at all four horizons; G1 improved on the prespecified Ridge comparator but was less accurate than F1. Equal-weight source-macro errors describe these four held-out operating domains, not a population of systems or sites. The findings do not establish transfer across manufacturers, stacks, capacities, or sites, and no electrochemical parameters were uniquely identified.

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Publication Details

Journal
Energies
Published
2026-10-30
DOI
https://doi.org/10.3390/en19215161
Primary Topic
Hybrid Renewable Energy Systems
Type
article
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article

Cross-Renewable Generalization of PEM Electrolyzer Hydrogen-Flow Forecasting: Multi-Horizon Evaluation and Physics Anchoring

Xianhuan Wu, Cenglin Yao, Lesheng Liu
6 links
Energies
Hybrid Renewable Energy Systems
article

Cross-Renewable Generalization of PEM Electrolyzer Hydrogen-Flow Forecasting: Multi-Horizon Evaluation and Physics Anchoring

Xianhuan Wu, Cenglin Yao, Lesheng Liu
article en

Abstract

Forecasting renewable-powered proton-exchange-membrane (PEM) electrolysis across unfamiliar power trajectories requires evaluation at the renewable-source level rather than within familiar profile families. We evaluated experimentally recorded hydrogen flow from one nominal 1.25 MW PEM-electrolyzer platform under four renewable-source operating domains—Simulated Wind, Simulated Wave, Historical Solar Photovoltaic, and Simulated Tidal—using leave-one-renewable-source-out validation, a 60 s process history, and direct 1, 5, 10, and 30 s horizons. Ridge, HistGradientBoosting, and LightGBM transferred usefully at short horizons but degraded as the horizon increased; Wave had negative R2 for all three models at 30 s. Source balancing and worst-source selection produced only local gains, and model-disagreement error awareness weakened with horizon. For one prespecified five-epoch Conv1d masked-reconstruction design and linear probe, self-supervised embeddings underperformed handcrafted descriptors in source-macro forecasting, without establishing a general limitation of self-supervision. A current-command-based operational gray-box anchor (F1) outperformed its residual-augmented version (G1) at all four horizons; G1 improved on the prespecified Ridge comparator but was less accurate than F1. Equal-weight source-macro errors describe these four held-out operating domains, not a population of systems or sites. The findings do not establish transfer across manufacturers, stacks, capacities, or sites, and no electrochemical parameters were uniquely identified.

EnergiesVol. 19(21)
Wuhan Business University (CN)
Affordable and clean energy
Openalex Percentile: Top 24%
Hybrid Renewable Energy Systems
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Cross-Renewable Generalization of PEM Electrolyzer Hydrogen-Flow Forecasting: Multi-Horizon Evaluation and Physics Anchoring — Xianhuan Wu, Cenglin Yao, et al. · Energies (2026) | TGRS Research Map | TGRS