Zero-Shot and Few-Shot Hydrogen Demand Forecasting with Time Series Foundation Models

Accurate hydrogen demand forecasting is essential for optimizing infrastructure investments in the emerging hydrogen economy, yet the scarcity of historical consumption data poses a fundamental challenge for conventional time series methods. We present a systematic evaluation of time series foundation models for hydrogen demand prediction, and propose DEFA (Data-Efficient Foundation model Adaptation), a two-stage framework designed to maximize few-shot adaptation performance under extreme data scarcity. DEFA first employs uncertainty-guided active sample selection, which leverages the foundation model's own zero-shot predictive variance to identify the most informative training sequences while maintaining diversity in embedding space. It then applies augmentation-enhanced consistency fine-tuning, combining temporal data augmentation with a consistency regularization loss that penalizes divergent predictions on semantically equivalent inputs, thereby mitigating overfitting when labeled data is scarce. We evaluate five pre-trained foundation models on two publicly available hydrogen datasets. Our experiments show that foundation models achieve strong zero-shot performance without domain-specific training, and that DEFA substantially improves few-shot adaptation efficiency, effectively halving the data requirement for a given accuracy target compared to standard random-sampling fine-tuning. Probabilistic foundation models further provide well-calibrated uncertainty estimates, enabling risk-aware infrastructure planning. To establish that these findings are not specific to the two hydrogen datasets, we further validate DEFA on six standard forecasting benchmarks (ETTh1, ETTh2, ETTm1, Electricity, Traffic, and M5 retail), where it delivers consistent 8.0–11.6% MAE reductions; we benchmark our selection rule against established active-learning strategies, extend the zero-shot comparison to recent foundation models, and provide a theoretical result linking the consistency objective to variance control in the few-shot regime. Our findings establish foundation models, enhanced by data-efficient adaptation, as a viable and practical solution for hydrogen demand forecasting in data-scarce environments.