Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

Summary This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building‐integrated photovoltaic‐thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function...

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Vydané v:	International journal of energy research Ročník 44; číslo 7; s. 5675 - 5695
Hlavní autori:	Shahsavar, Amin, Moayedi, Hossein, Al‐Waeli, Ali H. A., Sopian, Kamaruzzaman, Chelvanathan, Puvaneswaran
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Chichester, UK John Wiley & Sons, Inc 10.06.2020
Predmet:	Air flow Air masses Algorithms BIPVT Datasets Decision trees Design parameters exergetic performance Exergy Feasibility studies Flow rates Learning algorithms Machine learning Mass flow rate Mathematical models Multilayer perceptrons Optimization Parameter identification Parameters Performance evaluation performance evaluation criteria Performance indices Photovoltaic cells Photovoltaics Prediction models predictive model Radial basis function Statistical analysis Support vector machines Thermodynamics Training
ISSN:	0363-907X, 1099-114X
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