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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Vydané v:	Applied energy Ročník 390; s. 125863
Hlavní autori:	Yan, Peiliang, Wen, Chuang, Ding, Hongbing, Wang, Xuehui, Yang, Yan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 15.07.2025
Predmet:	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
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