Application of Multiple Linear Regression and Artificial Neural Networks in Analyses and Predictions of the Thermoelectric Performance of Solid Oxide Fuel Cell Systems

Meilin Lai, Daihui Zhang, Yu Li, Xiaolong Wu () and Xi Li
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Meilin Lai: School of Information Engineering, Nanchang University, Nanchang 330031, China
Daihui Zhang: School of Information Engineering, Nanchang University, Nanchang 330031, China
Yu Li: School of Information Engineering, Nanchang University, Nanchang 330031, China
Xiaolong Wu: School of Information Engineering, Nanchang University, Nanchang 330031, China
Xi Li: Key Laboratory of Education Ministry for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2024, vol. 17, issue 16, 1-30

Abstract: Solid oxide fuel cells (SOFCs) are an efficient, reliable and clean source of energy. Predictive modeling and analysis of their performance is becoming increasingly important, especially with the growing emphasis on sustainable development’s requirements. However, mathematical modeling is difficult due to the complexity of its internal structure. In this study, the system’s electricity generating performance and operational characteristics were analyzed using recent on-site monitoring data first. Then, based on Pearson’s correlation coefficient, some of the variables were selected to build two prediction models: an artificial neural network (ANN) model and a multiple linear regression (MLR) model. The models were evaluated on the basis of the normalized mean square error ( NRMSE ), which was 1.89% for the MLR model and 0.66% for the ANN model, with no overall bias. They were also compared with other existing models, and it was found that the two models used in this study have the advantage of high accuracy and low difficulty. Therefore, the models developed in this study can more accurately and effectively assess the SOFC system’s state and can better support work to improve the thermoelectric performance of SOFC systems.

Keywords: predictive modeling; fuel cell; solid oxide fuel cell system; artificial neural network; multiple linear regression; sustainable development (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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