Optimization and prediction of power density in proton exchange membrane fuel cells for green energy using advanced machine learning models: a comparative study

This study presents an advanced methodology that integrates experimental validation with machine learning (ML) models to predict and optimize power density in proton exchange membrane fuel cells (PEMFCs). The models considered include Linear Regression (LR), Stepwise Linear Regression (SLR), Tree Re...

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Bibliographic Details

Main Authors:	Katibi, Kamil Kayode, Shukla, Arun Kumar, Shitu, Ibrahim Garba, Alotaibi, Khalid M., Imran, Ahamad, Mojoyinola, Mubarak Olumide, Sirajudeen, Abdul Azeez Olayiwola
Format:	Article
Language:	en
Published:	Springer Science and Business Media Deutschland GmbH 2026
Subjects:	Chemical Engineering (all) Materials Science (all)
Online Access:	http://psasir.upm.edu.my/id/eprint/122921/1/122921.pdf http://psasir.upm.edu.my/id/eprint/122921/ https://link.springer.com/article/10.1007/s11581-025-06923-9?error=cookies_not_supported&code=82f4855e-5ca8-457d-9442-8f30a3c7aa9d
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