Open AccessBlack box modelling of a latent heat thermal energy storage system coupled with heat pipes
Author(s) -
Alessia Arteconi,
Francesco Ferracuti,
Roberto Tascioni,
Khamid Mahkamov,
Murat Kenisarin,
C. H. C. R. Costa,
Luisa F. Cabeza,
Álvaro de Gracia,
Elvedin Halimic,
David Mullen,
Kevin Lynn,
Luca Cioccolanti
Publication year - 2021
Publication title -
iop conference series: materials science and engineering
Language(s) - English
Resource type - Journals
ISSN - 1757-8981
DOI - 10.1088/1757-899x/1139/1/012010
Subject(s) - black box , thermal energy storage , latent heat , white box , computer data storage , phase change material , thermal , computer science , simulation , process engineering , engineering , environmental science , meteorology , artificial intelligence , thermodynamics , machine learning , computer hardware , physics
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%.