Evaluation of electrical efficiency of photovoltaic thermal solar collector

• Published in: Engineering applications of computational fluid mechanics Vol. 14; no. 1; pp. 545 - 565
• Main Authors: Ahmadi, Mohammad Hossein, Baghban, Alireza, Sadeghzadeh, Milad, Zamen, Mohammad, Mosavi, Amir, Shamshirband, Shahaboddin, Kumar, Ravinder, Mohammadi-Khanaposhtani, Mohammad
• Format: Journal Article
• Language: English
• Published: Hong Kong Taylor & Francis 01.01.2020; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: adaptive neuro-fuzzy inference system (ANFIS); Artificial neural networks; Flow velocity; Fuzzy logic; hybrid machine learning model; Inlet temperature; least square support vector machine (LSSVM); Machine learning; neural networks (NNs); Photovoltaic cells; photovoltaic-thermal (PV/T); Prediction models; Renewable energy; Solar radiation; Support vector machines
• ISSN: 1994-2060; 1997-003X
• DOI: 10.1080/19942060.2020.1734094

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.