E3S Web Conf.
Volume 113, 2019SUPEHR19 SUstainable PolyEnergy generation and HaRvesting Volume 1
|Number of page(s)||6|
|Section||Thermal and Electrical Hybrid Systems|
|Published online||21 August 2019|
Study on Fuel Utilization Dynamic model of a SOFC-GT Hybrid System Based on Deep Learning Technique
The Key Laboratory of Power Machinery and Engineering of Education Ministry, Shanghai Jiao Tong University, 200240 Shanghai, P. R. China
* Corresponding author: firstname.lastname@example.org
In order to perform operation management tasks, including state monitoring and control strategy optimization, of a solid oxide fuel cell-gas turbine (SOFC-GT) hybrid system, a data-driven dynamic model based on deep learning technique of long short term memory (LSTM) network is developed to predict the behaviours of fuel utilization. In addition, a LSTM model with unsupervised deep auto-encoder (DAE) method was developed to extract the feature from input data. The comparison performance between the common LSTM model and DAE-LSTM model was investigated. The results show that the DAE-LSTM model can enhance the prediction performance. Moreover, the effect of data size was investigated. The results demonstrate that the unsupervised DAE-LSTM model trained by large data size can further improve the prediction performance. The maximum error is only 0.00529, and average error decreases to 0.00025. In conclusions, the unsupervised DAE-LSTM model is an effective approach to predict dynamic behaviours.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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