An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis

Abstract

This study provides a data-driven analysis of a novel dataset of single-cell Anion Exchange Membrane water electrolyzers (AEMWE), operated under constant current load across multiple heterogeneous experimental campaigns. We train and evaluate a range of machine learning models with different complexity, including linear baselines, LSTMs and CNNs, to perform medium-term forecasting of the cell voltage degradation curve. The models are assessed within a rigorous training and evaluation framework specifically designed for heterogeneous industrial data.

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Cite

BibTeX RIS

BibTeX

@misc{veneriano2026efficient,
  title = {{An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis}},
  author = {Marco Veneriano and Ani Gjergji and Sebastiano Bellani and Andrea Riva and Vito Paolo Pastore and Matteo Santacesaria},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.37941},
  eprint = {2609.37941},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.37941},
}

RIS

TY  - UNPB
TI  - An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis
AU  - Marco Veneriano
AU  - Ani Gjergji
AU  - Sebastiano Bellani
AU  - Andrea Riva
AU  - Vito Paolo Pastore
AU  - Matteo Santacesaria
PY  - 2026
DA  - 2026/09/29
PB  - arXiv
DO  - 10.48550/arxiv.2609.37941
UR  - https://arxiv.org/abs/2609.37941
AB  - This study provides a data-driven analysis of a novel dataset of single-cell Anion Exchange Membrane water electrolyzers (AEMWE), operated under constant current load across multiple heterogeneous experimental campaigns. We train and evaluate a range of machine learning models with different complexity, including linear baselines, LSTMs and CNNs, to perform medium-term forecasting of the cell voltage degradation curve. The models are assessed within a rigorous training and evaluation framework specifically designed for heterogeneous industrial data.
ER  -