https://doi.org/10.1140/epjp/s13360-024-05723-w
Regular Article
Optimization of monocrystalline silicon solar cell using Box–Behnken design and machine learning models
1
Research Laboratory of Metrology and Energy Systems, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia
2
Laboratory of Electronics and Microelectronics LR99ES30, Faculty of Sciences, University of Monastir, Monastir, Tunisia
3
Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, Sousse, Tunisia
4
Urban Planning, and Building (ISTEUB), Higher Institute of Environmental Technologies, University of Carthage, Carthage, Tunisia
5
Quantum and Statistical Physics Laboratory, University of Monastir, Monastir, Tunisia
6
Department of Physics, College of Science, Majmaah University, 11952, Al Majma’ah, Saudi Arabia
Received:
11
March
2024
Accepted:
6
October
2024
Published online:
21
October
2024
This work integrates PC1D simulation, Box–Behnken design (BBD), and machine learning models (artificial neural network—ANN and particle swarm optimization-artificial neural network—PSO-ANN) to optimize monocrystalline silicon solar cells. Using the global desirability function, the optimal efficiency of 23.29% is obtained under certain conditions: p-type doping concentration (3.32 × 1017 cm−3), n-type doping concentration (6 × 1017 cm−3), textured wafer pyramid height (1 µm), textured wafer pyramid angle (80.67°), and temperature (20 °C). Notably, the PSO-ANN model outperforms the ANN model with an RMSE of 0.0149 and a correlation coefficient of 0.9997. This study demonstrates the effectiveness of advanced modeling and machine learning in increasing solar cell efficiency and highlights the superior performance of the PSO-ANN model.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
