A machine learning approach to PV-climate classification

doi:10.1016/j.renene.2025.123685

2025-06-13

Francisco Javier Triana de las Heras

Olindo Isabella

Malte Ruben Vogt

DOI：10.1016/j.renene.2025.123685

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摘要

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光伏（PV）系统性能与其运行所处的气候条件相关联。这引出了科彭-格伊格-光伏（KGPV）气候分类。KGPV是通过将四种辐照水平叠加在常用的科彭-格伊格气候区上创建的。该方法的潜在缺点在于，气候特征在分类过程中未以综合方式考虑，且KGPV区固有地依赖于降水。我们提出一种机器学习方法来解决这些不足并改进光伏气候分类。首先，使用监督学习来评估气候特征与光伏系统单位能量产量的相关性。我们发现，纳入最暗和最亮的辐照月份以及紫外线辐照可提高准确性，而风速、相对湿度、降水和年均日温差对准确性影响较小。随后，k均值聚类结合全面的定性分析，识别出一个基于七种气候特征和21个聚类的高光伏分类。与KGPV相比，发现了一种以中低温度和高辐照为特征的山地气候。此外，这种新的光伏气候分类将误差平方和减少了58%，明显表明这是一种更精确的光伏气候分类方法。

Keyword:

Photovoltaic

Solar energy

Machine learning (ML)

Performance

Climate zones

Climate classification

期刊

Renewable Energy

IF:

9.1

论文数:

2.6W

被引数:

12.1W

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