Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection

• Published in: Energy conversion and management Vol. 243; p. 114367
• Main Authors: Huo, Weiwei, Li, Weier, Zhang, Zehui, Sun, Chao, Zhou, Feikun, Gong, Guoqing
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.09.2021; Elsevier Science Ltd
• Subjects: administrative management; Algorithms; Artificial neural networks; data collection; Datasets; Deep learning; Electrochemistry; Electrodes; energy conversion; Fuel cell; Fuel cells; Fuel technology; hydrodynamics; Machine learning; Neural networks; Performance prediction; Polarization; prediction; Prediction models; Proton exchange membrane fuel cells; Protons; Random forest; Deep learning; Performance prediction; Random forest; Fuel cell

•Random forest algorithm is used to select important factors.•Performance prediction method of the FC employing convolutional neural networks (CNN)•Dropout layer and batch normalization are utilized to avoid model overfitting and improve model generalization. For optimizing the performance of the pr...