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

Marco Veneriano; Ani Gjergji; Sebastiano Bellani; Andrea Riva; Vito Paolo Pastore; Matteo Santacesaria

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arXiv · 2609.37941

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

Marco Veneriano·Ani Gjergji·Sebastiano Bellani·Andrea Riva·Vito Paolo Pastore·Matteo Santacesaria

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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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Marco Veneriano, Ani Gjergji, Sebastiano Bellani, Andrea Riva, Vito Paolo Pastore, Matteo Santacesaria. 2026-09-29. An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis. https://arxiv.org/abs/2609.37941

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