Classification of cell line Halm machine data in solar panel production factories using artificial intelligence models

İrfan Yilmaz, Demiral Akbar, Murat Şimşek


A solar energy module consists of solar cells that convert sunlight into electrical energy. The quality of these cells is the most important determinant of panel performance and lifespan. High-quality cells increase energy efficiency and extend panel life. Solar cells are typically composed of crystalline silicon, thin layers, and organic materials. Each material has its own advantages and disadvantages. However, what all cells have in common is that they produce electrical energy when exposed to solar radiation. Solar cells can be classified and ranked. This classification indicates the efficiency and performance of the cell. Solar energy modules are widely used to meet the energy needs of many homes and businesses. Accurately measuring cell performance can improve the overall efficiency of the panel. Therefore, AI (artificial intelligence) modeling offers many advantages in optimizing cell performance. The study yielded several benefits associated with modeling solar panel cells with artificial intelligence. Some of the benefits derived from this research are: Improved efficiency, Error detection and correction, Reduced maintenance costs, predictability, Increased production. These advantages demonstrate that AI modeling can help optimize solar panel cell performance.


artificial intelligence; machine learning; solar energy; pv-photovoltaic; energy quality; sigma principle

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1. Green MA. Solar Cells: Operating Principles, Technology, and System Applications. University of New South Wales Press; 2018.

2. Antunez EE, Gonzalez-Hernandez J, Dominguez A. Artificial neural network modeling of a photovoltaic panel considering temperature effects. International Journal of Energy Research 2019, 43(12), 5939-5950.

3. Kumar A, Kumar S. Artificial intelligence and machine learning for solar energy: a review. Renewable and Sustainable Energy Reviews, 2019, 101, 832-847.

4. Al-Samarraie MA, Kharraz OM. Artificial intelligence for solar energy applications: A review. Renewable and Sustainable Energy Reviews, 2019, 110, 265-278.

5. Al-Kayiem HH, Al-Khafaji ZS. Modeling and optimization of photovoltaic cells using artificial neural networks: A review. Renewable and Sustainable Energy Reviews, 2018, 82, 1811-1820.

6. Green MA, Emery K, Hishikawa Y, et al. Solar cell efficiency tables (version 52). Progress in Photovoltaics: Research and Applications, 2018, 26(7), 427-436.

7. Al-Ameri T, Abdalhadi D. Silicon Solar Cell: Review of Efficiency Enhancement Techniques. Journal of Electronic Materials, 2020, 49(6), 3856-3875.

8. Kato K, Yamaguchi M. Recent progress in crystalline silicon solar cells. Journal of Materials Research, 2019, 34(12), 2089-2101.

9. Ma Y, Yang Y, Yu X, et al. Artificial intelligence for energy management in future smart grids: A review. Renewable and Sustainable Energy Reviews, 2019, 104, 62-72.

10. Chaczko ZC, Gao J, Orlowska-Kowalska T, Kasprzak E. Artificial intelligence for renewable energy systems: A review. Renewable and Sustainable Energy Reviews, 2018, 81, 1851-1869.

11. Ahmadi MH, Moghaddam MP, Fathi SH. A review of renewable energy forecasting techniques sing artificial intelligence and machine learning algorithms. Renewable and Sustainable Energy Reviews, 2020, 133, 110292.

12. Alzahrani B, Kamel M. A survey of artificial intelligence techniques in renewable energy systems. Energies, 2020, 13(12), 3033.

13. Alavinasab M, Jalali A, Tabatabaei M. A review of artificial intelligence and machine learning approaches for short-term wind power forecasting. Renewable and Sustainable Energy Reviews, 2020, 124, 109778.

14. Zou Q, Zhang Z, Chen J. A review on artificial intelligence applications in wind energy systems. Journal of Cleaner Production, 2020, 255, 120281.

15. Pandey R, Chakrabarti S, Panigrahi BK. Integration of renewable energy sources and artificial intelligence: a comprehensive review. Journal of Cleaner Production, 2021, 294, 126167.

16. Shen S, Cao Y, Li L, et al. A review of machine learning applications in bioenergy systems. Renewable and Sustainable Energy Reviews, 2021, 135, 110211.

17. Wu T, Yang S, Gao Z, et al. A review on the applications of artificial intelligence in geothermal energy. Renewable and Sustainable Energy Reviews, 2020, 131, 110015.



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