人工神经网络对柔性碲化镉太阳能电池性能的预测

Sevinj Ganbarova, S. Akkoyun, Vusal Mamedov, Huseyn Mamedov
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引用次数: 0

摘要

超薄玻璃基板上的碲化镉太阳能电池轻巧灵活。柔性电池是技术领域广泛采用的模块。这些电池的柔性使其能够应对变形。其效率已达到 19%。在这项工作中,我们使用人工神经网络(ANN)方法来确定柔性碲化镉太阳能电池在弯曲和时间作用下的性能。我们预测了太阳能电池在弯曲前后的性能。根据使用文献中的实验数据进行的人工神经网络计算结果,人工神经网络估计值的 MSE 值在 0.06% 到 0.28% 之间。
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Predictions on Flexible CdTe Solar Cell Performances by Artificial Neural Networks
CdTe solar cells on ultra-thin glass substrates are light and flexible. Flexible cells are widely preferred modules in technological fields. The flexibility of these cells enables them to cope with deformations. The efficiency of these has reached 19%. In this work, we used artificial neural network (ANN) method for the determination the performance of flexible CdTe solar cells despite bending and time. The performances of the solar cell before and after bending have been predicted. According to the results from the ANN calculations using the experimental data in the literature, MSE values of ANN estimates range from 0.06% to 0.28%.
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