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Photovoltaic (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 MLP model 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 MLR technique exhibited the least ability to estimate the power generated by PV with L-shaped aluminum fins.
Czasopismo
Rocznik
Tom
Strony
336--344
Opis fizyczny
Bibliogr. 41 poz., rys., tab.
Twórcy
autor
- Applied Science Private University, Renewable Energy Technology Department, Amman 11937, Jordan
autor
- Al-Zaytoonah University of Jordan, Department of Alternative Energy Technology, Amman 11733, Jordan
autor
- Amman Arab University, Department of Renewable Energy Engineering, Amman 11953, Jordan
autor
- 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
Bibliografia
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- 37. Shiravi A.H., Firoozzadeh M. 2022. Performance assessment of a finned photovoltaic module exposed to an air stream: An experimental study. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44(11).
- 38. Stropnik R., Stritih U. 2016. Increasing the efficiency of PV panel with the use of PCM. Renewable Energy, 97, 671–679.
- 39. Valeh-e-Sheyda P., Rahimi M., Karimi E., Asadi M. 2013. Application of two-phase flow for cooling of hybrid microchannel PV cells: A comparative study. Energy Conversion and Management, 69, 122–130.
- 40. Verma S. Mohapatra S., Chowdhury S., Dwivedi G. 2021. Cooling techniques of the PV module: A Review. Materials Today: Proceedings, 38, 253–258.
- 41. Wang R., Wang J., Yuan W. 2019. Analysis and optimization of a microchannel heat sink with V-ribs using nanofluids for micro solar cells. Micromachines, 10(9), 620.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bc4349a3-c740-4078-8e52-8eb370652ab9