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可解释机器学习的应用,用于估计太阳辐射的直接和分散成分

Rial A Rajagukguk¹, Hyunjin Lee²

¹Department of Mechanical Engineering, Kookmin University, Seoul, 02707, South Korea.

Scientific reports

|March 3, 2025

概括

这项研究引入了一种新的机器学习方法,可以从全球水平辐射 (GHI) 中准确估计直接和分散的太阳辐射 (DNI和DHI). CatBoost模型表现出卓越的性能,强调湿度是改善太阳能预测的关键因素.

科学领域:

可再生能源可再生能源是可再生能源.
大气科学大气科学
机器学习机器学习

背景情况:

精确测量分散水平辐射 (DHI) 和直接正常辐射 (DNI) 对太阳能应用至关重要.
由于DNI/DHI测量设备的高成本,目前的仪器往往优先考虑全球水平辐射 (GHI).
现有的太阳分解模型试图从GHI中导出DNI和DHI,但成功程度不同.

研究的目的:

开发和评估一种基于机器学习的新型方法,用于从GHI中分离直接和分散的太阳辐射.
评估梯度提升机器学习模型在太阳辐射分解中的性能.
确定影响DNI和DHI估计准确性的关键气象参数.

主要方法:

使用了一种新的分离方法,使用机器学习算法与1分钟的时间分辨率数据.
训练并比较了三种梯度增强机器学习模型,使用来自10个全球气候不同站的数据.
应用沙普利增量解释 (SHAP) 来解释模型参数及其相互作用.

主要成果:

在所有测试站点上,CatBoost机器学习模型显著优于现有的太阳能分解模型.
在直接正常辐射 (DNI) 计算中,CatBoost在8.73%的RMSE中实现了最低的根平均平方误差.
湿度被确定为准确估计直接正常辐射 (DNI) 和分散水平辐射 (DHI) 的关键参数.

结论:

机器学习,特别是CatBoost模型,为太阳辐射分解提供了一种非常有效的方法.
开发的方法为获得太阳能能源应用的关键DNI和DHI数据提供了具有成本效益的替代方案.
了解气象因素 (如湿度) 的作用对于推进太阳能资源评估至关重要.

关键词:

扩散水平辐射的水平辐射.直接的正常辐射率是正常的.可解释的机器学习输入参数输入参数太阳的分解太阳的分解

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