Version 1
: Received: 10 June 2024 / Approved: 11 June 2024 / Online: 11 June 2024 (15:02:02 CEST)
How to cite:
Porowski, R.; Kowalik, R.; Gorzelnik, T.; Styś-Maniara, M.; Szeląg, B.; Komendołowicz, D.; Białek, A.; Ludynia, A.; Piłat-Rożek, M.; Łazuka, E.; Stanzani, E. Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator. Preprints2024, 2024060748. https://doi.org/10.20944/preprints202406.0748.v1
Porowski, R.; Kowalik, R.; Gorzelnik, T.; Styś-Maniara, M.; Szeląg, B.; Komendołowicz, D.; Białek, A.; Ludynia, A.; Piłat-Rożek, M.; Łazuka, E.; Stanzani, E. Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator. Preprints 2024, 2024060748. https://doi.org/10.20944/preprints202406.0748.v1
Porowski, R.; Kowalik, R.; Gorzelnik, T.; Styś-Maniara, M.; Szeląg, B.; Komendołowicz, D.; Białek, A.; Ludynia, A.; Piłat-Rożek, M.; Łazuka, E.; Stanzani, E. Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator. Preprints2024, 2024060748. https://doi.org/10.20944/preprints202406.0748.v1
APA Style
Porowski, R., Kowalik, R., Gorzelnik, T., Styś-Maniara, M., Szeląg, B., Komendołowicz, D., Białek, A., Ludynia, A., Piłat-Rożek, M., Łazuka, E., & Stanzani, E. (2024). Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator. Preprints. https://doi.org/10.20944/preprints202406.0748.v1
Chicago/Turabian Style
Porowski, R., Ewa Łazuka and Erica Stanzani. 2024 "Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator" Preprints. https://doi.org/10.20944/preprints202406.0748.v1
Abstract
Photovoltaic modules should pass series of tests and examinations, verifying the electrical and thermal characteristics of the module, before being released to the market, examining the properties of the modules should undergo appropriate durability and efficiency tests under real outdoor conditions with exposure to climatic conditions and under laboratory conditions. The performance of photovoltaic cells depends on many factors, such as solar irradiance, module operating temperature, installation location, weather conditions and module shading. In this paper, three selected photovoltaic modules were examined using a large-scale steady-state solar simulator. The current-voltage (I-V) characteristics of the photovoltaic modules were experimentally tested and analyzed. The next step was to implement a three-layer artificial neural network model (MLP). The experimental data obtained in the previous step coupled with output data from MLP were then used in global sensitivity analysis (GSA). Experiments carried out on a large-scale stationary solar simulator showed differences between the values declared by the manufacturer and the values obtained from measurements of PV modules. The first module tested achieved the maximum power point greater than that specified by the manufacturer, while the other two showed power drops; it was 85-87% for the second module, and 95-98% for the third, respectively. The performed global sensitivity analysis (GSA) for the MLP model showed that the parameters: eff (22.9) and Voc/V (14.19) have the largest effect on the power-voltage relationship, while U (7.29) has the smallest effect. The usefulness of machine learning (ML) methods in the comparative analysis of PV modules has been proved.
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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