使用机器学习预测发光太阳能缩器的太阳能收获效率
Rute A S Ferreira1, Sandra F H Correia2, Lianshe Fu3
1CICECO-Aveiro Institute of Materials, Physics Department, University of Aveiro, 3810-193, Aveiro, Portugal. rferreira@ua.pt.
Scientific reports
|February 20, 2024
概括
机器学习 (ML) 模型预测建筑集成光伏 (BIPV) 的光学特性. 这加速了用于窗户的高效发光太阳能缩器的开发,降低了成本并改善了材料选择.
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.
- 太阳能光伏发电是如何实现的
- 机器学习应用 机器学习应用
背景情况:
- 建筑集成光伏 (BIPV) 是一个新兴的太阳能技术.
- 发光太阳能缩器 (LSC) 提供了一种将传统窗户转化为能产生能量的表面的方法.
- 有效的光学材料设计对于提高BIPV性能至关重要.
研究的目的:
- 研究机器学习 (ML) 的应用,以了解和优化BIPV中的光学材料设计.
- 为了利用光发光度测量来预测LSC材料的光学特性.
- 简化开发和选择具有成本效益和高性能的BIPV材料.
主要方法:
- 使用可访问的光发光度测量作为ML模型的输入.
- 采用回归和集群技术来估计光学和功率转换效率.
- 使用平均绝对误差 (MAE) 验证了ML模型的准确性.
主要成果:
- 回归模型实现了10%的MAE,表明在预测光学转换效率方面具有很高的准确性.
- 无论是回归模型还是集群模型都显示出强烈的一致性,预测功率转换效率的最低MAE为7%.
- 在BIPV应用中,ML有效地预测了发光材料的光学特性.
结论:
- 机器学习为设计BIPV的光学材料提供了一种具有成本效益和效率的方法.
- ML模型显著简化了材料开发和选择过程.
- 该研究强调了ML在预测建筑物中太阳能收集发光材料性能方面的有效性.
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