The potential of machine learning to predict melting response time of phase change materials in triplex-tube latent thermal energy storage systems

Accurate prediction of the melting response time is vital for optimizing thermal energy storage systems, which play a key role in addressing the temporal mismatch between thermal energy demand and supply in the built environment. This study aims to quantitatively predict the melting response time of...

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Veröffentlicht in:	Applied energy Jg. 390; S. 125863
Hauptverfasser:	Yan, Peiliang, Wen, Chuang, Ding, Hongbing, Wang, Xuehui, Yang, Yan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.07.2025
Schlagworte:	Machine learning Melting response time Phase change material Thermal energy storage XGBoost algorithm Thermal energy storage XGBoost algorithm Phase change material Melting response time Machine learning
ISSN:	0306-2619
Online-Zugang:	Volltext
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