发表机构
Delft University of Technology; University of Naples Federico II（代尔夫特理工大学; 那不勒斯费德里科二世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了适用于光伏的机器学习气候分类框架，纳入能量产出与组件寿命，划分6类主气候簇及15类子簇，低温大陆性气候贴现寿命能量产出最高，可支撑光伏相关优化应用。
AI 中文摘要
为了弹性且可持续地满足未来能源需求，光伏（PV）组件必须部署在地理范围广泛、运行条件各异的区域，而这些条件会强烈影响光伏组件的性能和最优系统设计，因此针对光伏的专用气候分类具有重要应用价值。本研究开发了一种适用于光伏应用的气候分类框架，采用多种机器学习（ML）技术。在前期研究基础上，该方法不仅纳入了能量产出，还首次将组件寿命与气候相关的衰减纳入考虑。研究生成了包含12个输入特征和2个目标变量（即能量产出和组件寿命）的插值数据集。特征重要性分析显示，年总水平面辐照度和环境温度是最具影响力的预测因子。最准确的回归模型在能量产出预测上达到了0.007 MWh的均方根误差（RMSE），在寿命预测上达到了1.5年的RMSE。随后，将计算得到的特征重要性得分整合到层次聚类框架中，得到6个主要气候簇（热带、沙漠、大陆性、温带、北方、极地）和15个对应的子簇。分析表明，低温大陆性气候的贴现寿命能量产出最高。这些结果可支持光伏组件优化、系统选址决策和对比性能研究等广泛应用。
英文摘要
To resiliently and sustainably meet our future energy demand, photovoltaic (PV) modules must be deployed across a broad and diverse range of geographical regions with varying operating conditions. As these conditions strongly affect both performance and optimal system design, a dedicated PV-specific climate classification can be of great use. In this work, we develop a climate classification framework tailored to PV applications using a variety of machine learning (ML) techniques. Building on previous studies, our approach incorporates both energy yield, and for the first time, also the module lifetime with climate dependent degradation. We generate an interpolated dataset containing twelve input features and two target variables (i.e. energy yield and module lifetime). Feature importance analysis shows that annual global horizontal irradiation and ambient temperature are the most influential predictors. The most accurate regression model achieves root mean square errors (RMSE) of 0.007 MWh for energy yield and 1.5 years for lifetime prediction. The calculated feature importance scores are then integrated into a hierarchical clustering framework, resulting in 6 primary climate clusters (Tropical, Desert, Continental, Temperate, Boreal, and Polar) and 15 corresponding subclusters. Our analysis shows that the low temperature continental climate offers the highest discounted lifetime energy yield. These results can support a wide range of applications, including PV module optimization, system siting decisions, and comparative performance studies.
Comments16 pages, 8 figures. Manuscript is submitted to Progress in Photovoltaics
