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http://dx.doi.org/10.7316/KHNES.2020.31.5.436

Performance Prediction Model of Solid Oxide Fuel Cell Stack Using Deep Neural Network Technique  

LEE, JAEYOON (Department of Clean Fuel and Power Generation, Korea Institute of Machinery and Materials)
PINEDA, ISRAEL TORRES (Department of Clean Fuel and Power Generation, Korea Institute of Machinery and Materials)
GIAP, VAN-TIEN (Department of Clean Fuel and Power Generation, Korea Institute of Machinery and Materials)
LEE, DONGKEUN (Department of Clean Fuel and Power Generation, Korea Institute of Machinery and Materials)
KIM, YOUNG SANG (Department of Clean Fuel and Power Generation, Korea Institute of Machinery and Materials)
AHN, KOOK YOUNG (Department of Clean Fuel and Power Generation, Korea Institute of Machinery and Materials)
LEE, YOUNG DUK (Department of Clean Fuel and Power Generation, Korea Institute of Machinery and Materials)
Publication Information
Transactions of the Korean hydrogen and new energy society / v.31, no.5, 2020 , pp. 436-443 More about this Journal
Abstract
The performance prediction model of a solid oxide fuel cell stack has been developed using deep neural network technique, one of the machine learning methods. The machine learning has been received much interest in various fields, including energy system mo- deling. Using machine learning technique can save time and cost requried in developing an energy system model being compared to the conventional method, that is a combination of a mathematical modeling and an experimental validation. Results reveal that the mean average percent error, root mean square error, and coefficient of determination (R2) range 1.7515, 0.1342, 0.8597, repectively, in maximum. To improve the predictability of the model, the pre-processing is effective and interpolative machine learning and application is more accurate than the extrapolative cases.
Keywords
Solid oxide fuel cell; Deep learning; Convolutional neural network; Performance prediction; Regression;
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Times Cited By KSCI : 4  (Citation Analysis)
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