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Estimation of Photovoltaic Module Performance with L-Shaped Aluminum Fins Using Weather Data
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1
Renewable Energy Technology Department, Applied Science Private University, Amman 11937, Jordan
 
2
Department of Alternative Energy Technology, Faculty of Engineering and Technology, Al-Zaytoonah University of Jordan, Amman 11733, Jordan
 
3
Department of Renewable Energy Engineering, Amman Arab University, Amman 11953, Jordan
 
4
Department of Renewable Energies and Decentralized Energy Supplying, Faculty of Environmental Engineering and Applied Informatics, University of Applied Sciences and Arts, Campusallee 12, 32657 Lemgo, Germany
 
These authors had equal contribution to this work
 
 
Corresponding author
Eman Abdelhafez   

Department of Alternative Energy Technology, Faculty of Engineering and Technology, Al-Zaytoonah University of Jordan, Amman 11733, Jordan
 
 
J. Ecol. Eng. 2024; 25(1):336-344
 
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ABSTRACT
PV power prediction is vital for efficient and effective solar energy utilization within the energy ecosystem. It enables grid stability, cost savings, and the seamless integration of solar power into the broader energy infrastructure. In this work, previously obtained data on the estimation of the power produced by a PV, which is cooled by L-shaped aluminum fins attached to the backside of the PV at different spacings, is used to predict the power produced by the PV. This is achieved by employing both neural network models and multiple linear regression (MLR) techniques to assess the correlation between power generated by PV with L-shaped aluminum fins and its input variables. Two distinct approaches were employed for this purpose. The first approach involved the conventional MLR model, while the second utilized a neural network, specifically the Multilayer Perceptron (MLP) model. The estimated outcomes were subsequently compared against the previously measured data. The MLR technique showed a great ability to identify the relationship between input and output variables, it was noted. The statistical error study provided evidence of data mining's acceptable accuracy when using the MLP model. Conversely, the results indicated that the Multilayer Perceptron Network (MLP) model exhibited the least ability to estimate the power generated by PV with L-shaped aluminum Fins.
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