Evaluation of electrical efficiency of photovoltaic thermal solar collector

Medientyp: E-Artikel
Titel: Evaluation of electrical efficiency of photovoltaic thermal solar collector
Beteiligte: Ahmadi, Mohammad Hossein [VerfasserIn]; Baghban, Alireza [VerfasserIn]; Sadeghzadeh, Milad [VerfasserIn]; Zamen, Mohammad [VerfasserIn]; Mosavi, Amir [VerfasserIn]; Shamshirband, Shahaboddin [VerfasserIn]; Kumar, Ravinder [VerfasserIn]; Mohammadi-Khanaposhtani, Mohammad [VerfasserIn]
Erschienen: 26.02.2020
Erschienen in: Engineering applications of computational fluid mechanics ; 14(2020), 1, Seite 545-565
Sprache: Englisch
DOI: 10.1080/19942060.2020.1734094
ISSN: 1997-003X
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Beschreibung: In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
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