This paper presents black box models to represent a LHTESS (Latent Heat Thermal Energy Storage System) coupled with heat pipes, aimed at increasing the storage performance and at decreasing the time of charging/discharging. The presented storage system is part of a micro solar CHP plant and the developed model is intended to be used in the simulation tool of the overall system, thus it has to be accurate but also fast computing. Black box data driven models are considered, trained by means of numerical data obtained from a white box detailed model of the LHTESS and heat pipes system. A year round simulation of the system during its normal operation within the micro solar CHP plant is used as dataset. Then the black box models are trained and finally validated on these data. Results show the need for a black box model that can take into account the different seasonal performance of the LHTESS. In this analysis the best fit was achieved by means of Random Forest models with an accuracy higher than 90%.
This study is a part of the Innova MicroSolar Project, funded in the framework of the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 723596). Prof. Cabeza would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREA is certified agent TECNIO in the category of technology developers from the Government of Catalonia. Dr. Alvaro de Gracia has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 712949.
Anglès
PCM; Heat pipes; Black box models; ARX; NARX; Random Forest
IOP Publishing
Reproducció del document publicat a https://doi.org/10.1088/1757-899X/1139/1/012010
Proceedings of the Join 19th International Heat Pipe Conference and 13th International Heat Pipe Symposium (Joint 19th IHPC and 13th IHPS), Pisa, Italia del 10 al 14 de juny de 2018
info:eu-repo/grantAgreement/EC/H2020/723596/EU/Innova MicroSolar
info:eu-repo/grantAgreement/EC/H2020/712949/EU/TECNIOspring PLUS
cc-by (c) Alessia Arteconi et al., 2021
https://creativecommons.org/licenses/by/3.0/
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