Machine Learning Prediction Models of Electrical Efficiency of Photovoltaic-Thermal Collectors

Mohammad Hossein Ahmadi; Alireza Baghban; Ely Salwana; Milad Sadeghzadeh; Mohammad Zamen; Shahab Shamshirband; Ravinder Kumar

doi:10.20944/preprints201905.0033.v1

Preprint Article Version 1 This version is not peer-reviewed

Machine Learning Prediction Models of Electrical Efficiency of Photovoltaic-Thermal Collectors

Mohammad Hossein Ahmadi , Alireza Baghban , Ely Salwana , Milad Sadeghzadeh , Mohammad Zamen , Shahab Shamshirband ^* , Ravinder Kumar

Version 1 : Received: 1 May 2019 / Approved: 6 May 2019 / Online: 6 May 2019 (08:10:59 CEST)

How to cite: Ahmadi, M.H.; Baghban, A.; Salwana, E.; Sadeghzadeh, M.; Zamen, M.; Shamshirband, S.; Kumar, R. Machine Learning Prediction Models of Electrical Efficiency of Photovoltaic-Thermal Collectors. Preprints 2019, 2019050033 (doi: 10.20944/preprints201905.0033.v1). Ahmadi, M.H.; Baghban, A.; Salwana, E.; Sadeghzadeh, M.; Zamen, M.; Shamshirband, S.; Kumar, R. Machine Learning Prediction Models of Electrical Efficiency of Photovoltaic-Thermal Collectors. Preprints 2019, 2019050033 (doi: 10.20944/preprints201905.0033.v1).

Abstract

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.

Subject Areas

PV/T collector; electrical efficiency; renewable energy; intelligent models; optimization; machine learning; multilayer perceptron (MLP), artificial neural network (ANN); adaptive neuro-fuzzy inference system (ANFIS); least squares support vector machine (LSSVM); photovoltaic-thermal (PV/T)

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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