高精度机器学习方法用于在可变辐射强度下预测矿太阳能电池的J-V特性
1Department of Electronics and Automation, Selçuk University, Konya, Türkiye, Turkey. aytoprak@selcuk.edu.tr.
Scientific reports
|November 21, 2025
概括
一个新的机器学习模型准确地预测了矿太阳能电池 (PSC) 的性能. 这种人工神经网络 (ANN) 提供了一种快速,经济高效的解决方案,用于在不同光线条件下建模PSC J-V特征.
科学领域:
- 材料科学 材料科学 材料科学
- 可再生能源可再生能源是可再生能源.
- 人工智能的人工智能
背景情况:
- 矿太阳能电池 (PSC) 具有高效率和低制造成本.
- 在不同辐射强度下的PSCs的精确J-V特征建模与传统方法是困难和耗时的.
研究的目的:
- 开发一种基于机器学习的方法,用于精确的PSC性能建模.
- 克服PSC表征的传统实验方法的局限性.
主要方法:
- 一个多层感知器 (MLP) 人工神经网络 (ANN) 被训练.
- 该ANN模型使用了一个大型的模拟生成的数据集,辐射强度和电压作为输入,电流作为输出.
- 为了高效的模型训练和融合,采用了莱文伯格-马奎特算法.
主要成果:
- 该ANN实现了高预测准确度,相关系数超过0.9996.6.
- 在训练,验证和测试数据集中观察到非常低的平均平方误差 (MSE) 值.
- 预测的J-V曲线与模拟数据密切匹配,证明了模型的可靠性.
结论:
- 开发的ANN提供了一个具有成本效益和可扩展的方法来建模PSC性能.
- 这种方法可以加速优化和部署先进的光伏技术.
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