Improving prediction of anti-reflective film-coated photovoltaic solar panel efficiency by integrating bayesian with machine learning algorithms
DOI:
https://doi.org/10.4025/actascitechnol.v47i1.70900Palavras-chave:
Machine learning; bayesian algorithm; random forest; multi regression algorithm.Resumo
This manuscript explores machine learning models for predicting the efficiency of anti-reflective film-coated photovoltaic solar panels in the South Indian climate. Three models—standard artificial neural network (ANN), random forest algorithm, and multilinear regression—were developed and compared. 80% of the dataset was used for training and 20% for testing. The random forest model demonstrated superior effectiveness with a lower prediction error. Bayesian optimization refined both ANN and random forest models. Experiments yielded an average solar panel efficiency of 16.79%, with performance indicators (coefficient of determination) of 0.96966, 0.93466, 0.98419 and mean absolute percentage errors of 7.518, 10.658, 5.089%. Bayesian optimization improved the traditional ANN model by up to 36.2%. Random forest exhibited lower sensitivity to hyperparameters compared to ANN. Two important parameters such as coating thickness and solar insolation were identified by feature sensitivity analysis. In terms of accuracy and robustness, random forest outperformed in predicting anti-reflective film-coated photovoltaic solar panel efficiency.
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Referências
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Foorginezhad, S., & Zerafat, M. M. (2019). Fabrication of stable fluorine-free superhydrophobic fabrics for anti-adhesion and self-cleaning properties. Applied Surface Science, 464, 458-471. https://doi.org/10.1016/j.apsusc.2018.09.058
Ghafouri-Kesbi, F., Rahimi-Mianji, G., Honarvar, M., & Nejati-Javaremi, A. (2016). Predictive ability of random forests, boosting, support vector machines and genomic best linear unbiased prediction in different scenarios of genomic evaluation. Animal Production Science, 57(2), 229-236. https://doi.org/http://dx.doi.org/10.1071/AN15538
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Haji, S. H., & Abdulazeez, A. M. (2021). Comparison of optimization techniques based on gradient descent algorithm: A review. PalArch's Journal of Archaeology of Egypt/Egyptology, 18(4), 2715-2743.
Hong, Y., Hou, B., Jiang, H., & Zhang, J. (2020). Machine learning and artificial neural network accelerated computational discoveries in materials science. Wiley Interdisciplinary Reviews: Computational Molecular Science, 10(3), e1450. https://doi.org/10.1002/wcms.1450
Huh, J. Y., Ma, S. H., Kim, K., Choi, E. H., Lee, H. J., Lee, H. U., & Hong, Y. C. (2019). Facile, rapid, one-pot synthesis of hydrogenated TiO2 by using an atmospheric-pressure plasma jet submerged in solution. Scripta Materialia, 162, 9-13. https://doi.org/10.1016/j.scriptamat.2018.10.028
Kulkarni, P., Sreekanth, V., Upadhya, A. R., & Gautam, H. C. (2022). Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2. 5 from high-resolution satellite aerosol optical depth. Atmospheric Environment, 282, 119164. https://doi.org/10.1016/j.atmosenv.2022.119164
Laseinde, O. T., & Ramere, M. D. (2021). Efficiency Improvement in polycrystalline solar panel using thermal control water spraying cooling. Procedia Computer Science, 180, 239-248. https://doi.org/10.1016/j.procs.2021.01.161
Liu, F., Tait, S., Schellart, A., Mayfield, M., & Boxall, J. (2020). Reducing carbon emissions by integrating urban water systems and renewable energy sources at a community scale. Renewable and Sustainable Energy Reviews, 123, 109767, https://doi.org/10.1016/j.rser.2020.109767
Liu, H., Li, G., Cumberland, W. G., & Wu, T. (2005). Testing statistical significance of the area under a receiving operating characteristics curve for repeated measures design with bootstrapping. Journal of Data Science, 3(3), 257-278. https://doi.org/10.6339/JDS.2005.03(3).206
Machello, C., Baghaei, K. A., Bazli, M., Hadigheh, A., Rajabipour, A., Arashpour, M., & Hassanli, R. (2024). Tree-based machine learning approach to modelling tensile strength retention of Fibre Reinforced Polymer composites exposed to elevated temperatures. Composites Part B: Engineering, 270, 111132. https://doi.org/10.1016/j.compositesb.2023.111132
Mahendran, M., Lizotte, D., & Bauer, G. R. (2022). Describing intersectional health outcomes: an evaluation of data analysis methods. Epidemiology, 33(3), 395-405. https://doi.org/10.1097/ede.0000000000001466
Maind, S. B., & Wankar, P. (2014). Research paper on basic of artificial neural network. International Journal on Recent and Innovation Trends in Computing and Communication, 2(1), 96-100. https://doi.org/10.17762/ijritcc.v2i1.2920
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2025-03-25
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Sankaran, D. ., Pappan, B. ., & Selvaraj, S. . (2025). Improving prediction of anti-reflective film-coated photovoltaic solar panel efficiency by integrating bayesian with machine learning algorithms. Acta Scientiarum. Technology, 47(1), e70900. https://doi.org/10.4025/actascitechnol.v47i1.70900
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