揭示矿太阳能电池中的主导重组损失,使用基于XGBoost的机器学习方法
Basir Akbar1, Hilal Tayara2, Kil To Chong3,4
1Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, South Korea.
iScience
|February 29, 2024
概括
机器学习准确地预测了矿太阳能电池 (PSC) 中的重组损失,提高了能源设备的效率. 光伏技术的进步将能量的损失降至最低,以实现更好的太阳能转换.
科学领域:
- 光伏技术 光伏技术
- 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.
背景情况:
- 矿太阳能电池 (PSC) 是第三代光伏研究的一个关键领域.
- 主要的重组损失,特别是在谷物边界 (GB) 和接口上,限制了PSC的效率.
- 开发预测和减轻这些损失的方法对于推进太阳能技术至关重要.
研究的目的:
- 实施机器学习 (ML) 算法,用于预测PSC中的主导重组损失.
- 确定有助于重组的关键因素,包括粒度边界,接口和带对带重组.
- 开发一个强大的模型来提高矿太阳能电池的效率.
主要方法:
- 使用极端梯度增强 (XGBoost) 分类器来预测重组损失.
- 采用7倍交叉验证,以确保模型的通用性和稳定性.
- 利用Optuna进行超参数优化和SHAP对超过200万个模拟数据点进行特征影响分析.
主要成果:
- 在使用XGBoost模型预测再组合损失时获得了85%的准确性.
- 确定光强度和开放电路电压作为重要的输入参数.
- 拟议的模型在评估再组合损失时,与最先进的方法相比,表现出更高的性能.
结论:
- 机器学习,特别是XGBoost,在预测PSC中的主导重组损失方面是有效的.
- 开发的模型为设计更高效,更可靠的矿太阳能电池提供了一条途径.
- 这项研究通过解决关键损失机制,有助于提高高性能光伏设备的进步.
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