Preprint Article Version 1 This version is not peer-reviewed

Evaluation of Electrical Efficiency of Photovoltaic Thermal Solar Collector

Version 1 : Received: 12 February 2020 / Approved: 14 February 2020 / Online: 14 February 2020 (02:53:47 CET)

How to cite: Ahmadi, M.H.; Baghban, A.; Sadeghzadeh, M.; Zamen, M.; Mosavi, A.; Shamshirband, S.; Kumar, R.; Mohammadi-Khanaposhtani, M. Evaluation of Electrical Efficiency of Photovoltaic Thermal Solar Collector. Preprints 2020, 2020020181 (doi: 10.20944/preprints202002.0181.v1). Ahmadi, M.H.; Baghban, A.; Sadeghzadeh, M.; Zamen, M.; Mosavi, A.; Shamshirband, S.; Kumar, R.; Mohammadi-Khanaposhtani, M. Evaluation of Electrical Efficiency of Photovoltaic Thermal Solar Collector. Preprints 2020, 2020020181 (doi: 10.20944/preprints202002.0181.v1).

Abstract

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the 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 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.

Subject Areas

renewable energy; neural networks (NNs); adaptive neuro-fuzzy inference system (ANFIS); least square support vector machine (LSSVM); photovoltaic-thermal (PV/T); hybrid machine learning model

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