Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method

Mumin Rao, Li Wang, Chuangting Chen, Kai Xiong, Mingfei Li, Zhengpeng Chen, Jiangbo Dong, Junli Xu and Xi Li
Additional contact information
Mumin Rao: Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China
Li Wang: Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Chuangting Chen: Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China
Kai Xiong: Guangdong Energy Group Co., Ltd., Guangzhou 510630, China
Mingfei Li: Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China
Zhengpeng Chen: Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China
Jiangbo Dong: Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China
Junli Xu: Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China
Xi Li: Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2022, vol. 15, issue 9, 1-15

Abstract: A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state.

Keywords: solid oxide fuel cell; state prediction; multi-step prediction; deep learning (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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