预测和分析印度极端气候地区的SAPV系统的季节性GHI
Aadyasha Patel1, Gnana Swathika O V2
1School of Electrical Engineering, Vellore Institute of Technology, Chennai, India.
Scientific reports
|November 12, 2025
概括
高斯过程回归 (GPR) 在太阳能应用的季节性全球水平辐射 (GHI) 预测中表现出色. 与其他方法相比,这种机器学习模型提供了更高的准确性和可靠性,这对于光伏系统至关重要.
科学领域:
- 预测可再生能源的使用情况
- 机器学习应用 机器学习应用
- 太阳辐射预测预测
背景情况:
- 准确的长期太阳辐射预测对于优化可再生能源系统至关重要.
- 全球水平辐射 (GHI) 预测的传统方法经常被机器学习 (ML) 方法所超越.
- 使用ML利用历史GHI和气候数据可以发现复杂的模式,以改善预测.
研究的目的:
- 调查和比较不同ML模型的性能,用于在印度多种气候中季节性GHI预测.
- 确定最准确和最可靠的ML模型,用于在各种地理和气候条件下预测GHI.
- 评估ML模型对于成本效益高的独立光伏 (SAPV) 系统的适用性.
主要方法:
- 他们利用了来自印度四个地点 (奈,贾斯勒梅尔,雷赫,莫斯辛拉姆) 的国家太阳辐射数据库的数据.
- 通过Spearman的相关性和相互信息进行了严格的数据质量评估和特征选择.
- 训练和评估了三个ML模型:有效线性回归 (ELR),回归树 (RT) 和高斯过程回归 (GPR).
主要成果:
- 高斯过程回归 (GPR) 在所有测试地点的季节性GHI预测中表现出卓越的准确性.
- GPR实现了最低的根平均平方误差 (RMSE) 和平均绝对误差 (MAE),R2为0.9999.9.
- 与ELR相比,GPR在RMSE (189.1%的减少) 和MAE (190.09%的减少) 中显示出显著的改善.
- 此外,GPR的表现也超过了RT,RMSE (124.05%) 和MAE (111.1%) 的显著降低.
结论:
- 在印度多样化的气候条件下,GPR是最准确,最可靠的ML模型用于季节性GHI预测.
- 高精度的GPR使其成为可靠和经济高效的独立光伏 (SAPV) 系统的理想选择.
- GPR有效地捕获了几乎所有数据集的变化,证实了其在太阳辐射预测方面的卓越表现.
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