Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide

•The specific heat capacity (SCH) of CuO/water nanofluids was accurately modelled using SVR and ANN models.•The SVR model has a higher prediction accuracy than the ANN model.•Both SVR and ANN models performed much better than the existing empirical relations. This paper presents the modelling of the...

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Veröffentlicht in:	Solar energy Jg. 197; S. 485 - 490
Hauptverfasser:	Alade, Ibrahim Olanrewaju, Abd Rahman, Mohd Amiruddin, Abbas, Zulkifly, Yaakob, Yazid, Saleh, Tawfik A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 01.02.2020 Pergamon Press Inc
Schlagworte:	Artificial neural network Artificial neural networks Bayesian algorithm Copper oxides Heat Heat transfer Learning algorithms Learning theory Machine learning Model accuracy Nanofluids Nanoparticles Neural networks Regression analysis Solar energy Specific heat Specific heat capacity Support vector machines Support vector regression Support vector regression Bayesian algorithm Artificial neural network Specific heat capacity Nanofluids
ISSN:	0038-092X, 1471-1257
Online-Zugang:	Volltext
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