| Autor: |
Mumin Rao, Li Wang, Chuangting Chen, Kai Xiong, Mingfei Li, Zhengpeng Chen, Jiangbo Dong, Junli Xu, Xi Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Energies, Vol 15, Iss 9, p 3099 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1996-1073 |
| DOI: |
10.3390/en15093099 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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