Evaluation of electrical efficiency of photovoltaic thermal solar collector

• URL: http://arxiv.org/abs/2002.05542v1
• Date: Tue, 11 Feb 2020 21:11:54 GMT
• Title: Evaluation of electrical efficiency of photovoltaic thermal solar collector
• Authors: Mohammad Hossein Ahmadi, Alireza Baghban, Milad Sadeghzadeh, Mohammad Zamen, Amir Mosavi, Shahaboddin Shamshirband, Ravinder Kumar, Mohammad Mohammadi-Khanaposhtani
• Abstract summary: This study uses machine learning methods to predict the thermal performance of a photovoltaic-thermal solar collector (PV/T) The inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. The proposed LSSVM model outperformed ANFIS and ANNs models.
• Score: 1.9604116035607555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among renewable energies. 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 the 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 inputs 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 approaches and evaluate their performance. The proposed LSSVM model outperformed 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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