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. Preprints2019, 2019050033. https://doi.org/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. https://doi.org/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. Preprints2019, 2019050033. https://doi.org/10.20944/preprints201905.0033.v1
APA Style
Ahmadi, M.H., Baghban, A., Salwana, E., Sadeghzadeh, M., Zamen, M., Shamshirband, S., & Kumar, R. (2019). Machine Learning Prediction Models of Electrical Efficiency of Photovoltaic-Thermal Collectors. Preprints. https://doi.org/10.20944/preprints201905.0033.v1
Chicago/Turabian Style
Ahmadi, M.H., Shahab Shamshirband and Ravinder Kumar. 2019 "Machine Learning Prediction Models of Electrical Efficiency of Photovoltaic-Thermal Collectors" Preprints. https://doi.org/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.
Computer Science and Mathematics, Computational Mathematics
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.