Papers for

energy systems developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Machine learning predicts voltage decline in water electrolyzers

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.

Tue 29 SeptMachine Learning

The gist

Water electrolyzers split water into hydrogen and oxygen but lose efficiency over time. The authors used machine learning models to predict how the voltage needed to run these devices changes as they age. They tested several types of models on a new dataset from various experiments. This approach helps foresee equipment degradation, which can improve maintenance and reliability.

Open → 2609.37941v1