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- W2969862308 abstract "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." @default.
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- W2969862308 date "2019-01-01" @default.
- W2969862308 modified "2023-09-27" @default.
- W2969862308 title "Study on Fuel Utilization Dynamic model of a SOFC-GT Hybrid System Based on Deep Learning Technique" @default.
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- W2969862308 doi "https://doi.org/10.1051/e3sconf/201911302010" @default.
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