Zouhour Rhaim, Fraj Echouchene, Sabra Habli, Mohamed Hichem Gazzah, Mohammed A. Albedah, Hafedh Belmabrouk
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引用次数: 0
Abstract
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.
期刊介绍:
The aims of this peer-reviewed online journal are to distribute and archive all relevant material required to document, assess, validate and reconstruct in detail the body of knowledge in the physical and related sciences.
The scope of EPJ Plus encompasses a broad landscape of fields and disciplines in the physical and related sciences - such as covered by the topical EPJ journals and with the explicit addition of geophysics, astrophysics, general relativity and cosmology, mathematical and quantum physics, classical and fluid mechanics, accelerator and medical physics, as well as physics techniques applied to any other topics, including energy, environment and cultural heritage.