Preprint

Evaluation of Electrical Efficiency of Photovoltaic Thermal Solar Collector

Mohammad Hossein AhmadiFaculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, IranAlireza BaghbanChemical engineering Department, Amirkabir University of Technology, Mahshahr, IranMilad SadeghzadehDepartment of Renewable Energies, Faculty of New Sciences and Technologies, University of Tehran, Tehran, IranMohammad ZamenFaculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, IranAmir MosaviInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamShahaboddin ShamshirbandDepartment for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, VietnamRavinder KumarDepartment of Mechanical Engineering, Lovely Professional University, Punjab, IndiaMohammad Mohammadi‐KhanaposhtaniFouman Faculty of Engineering, College of Engineering, University of Tehran, Tehran, Iran

2020en

ABI

Annotatsiya

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.

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Identifikatorlar

DOI: 10.1080/19942060.2020.1734094

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