Machine Learning Surrogate Dynamical System Model for Thermal Energy Storage
DOI:
https://doi.org/10.18687/LACCEI2024.1.1.1795Palabras clave:
machine learning, surrogate model, dynamical system, thermal energy storageResumen
Abstract– A thermal energy storage (TES) can serve as a mean of minimizing energy losses when there is fluctuation of energy demand. A coupled fluid and conduction thermal model are performed to obtain the history of the temperature profile over 500 timesteps simulations. Results files are generated in text format and imported as numerical arrays in Python programing. These results are used to train a deep learning algorithm based on convolutional and dense layers. Two of these architectures are presented here. Under this architecture, results can match validation data for a certain number of cases with relatively low errors
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2024-07-27
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Derechos de autor 2024 LACCEI
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Esta obra está bajo una Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.
LACCEI conserva el copyright de todos los artículos publicados bajo los términos de su acuerdo de transferencia de copyright. Como titular del copyright, LACCEI distribuye los artículos al público bajo la Licencia Internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0 (CC BY-NC-SA 4.0).
Cómo citar
Diaz Sierra, E. F., Cruz De Jesus, F. M., Jimenez Ortiz, J. J., Lebron Santana, A. J., Nuñez Rios, J. J., & Traverso, L. M. (2024). Machine Learning Surrogate Dynamical System Model for Thermal Energy Storage. LACCEI, 1(10). https://doi.org/10.18687/LACCEI2024.1.1.1795