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.

Machine Learning (cs.LG); Systems and Control (eess.SY); Applied Physics (physics.app-ph)

arXiv:2609.37941 [cs.LG]

(or arXiv:2609.37941v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.37941 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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