AI 中文总结
本文提出统计与深度学习方法预测光伏系统聚合物户外降解，对比模型性能，为光伏可靠性提供关键发现。
AI 中文摘要
聚合物材料广泛应用于光伏（PV）系统，因此了解其使用寿命对确保光伏系统的可靠性能至关重要。光伏系统中聚合物材料的主要失效机制是由紫外线（UV）辐射引发的光降解。降解建模为预测使用寿命提供了框架，其中关键步骤是构建降解路径的预测模型。本文提出了用于预测光伏系统中聚合物部件户外降解的统计与机器学习方法。我们描述了基于室内实验室测试数据开发预测模型的研究设计与数据收集过程，随后将这些模型扩展至具有时变环境变量的户外现场条件，并通过模拟量化预测不确定性。本文还探索了深度学习（DL）方法，并对不同建模方法的结果进行了比较。参数统计模型在各数据集上表现出良好的拟合度与预测性能，且通过融入物理和化学知识展现出更强的鲁棒性；DL模型在捕捉复杂协变量关系方面具有灵活性，常能给出准确预测，但在不同数据集上的鲁棒性较弱。本文最后总结了关键发现及其对光伏可靠性的意义。
英文摘要
Polymeric materials are widely used in photovoltaic (PV) systems, making it essential to understand their service life to ensure reliable PV performance. The primary failure mechanism of polymeric materials in PV systems is photodegradation caused by ultraviolet (UV) radiation. Degradation modeling provides a framework for predicting service life, with a key step being the development of predictive models for degradation paths. This paper presents statistical and machine learning approaches for predicting the outdoor degradation of polymeric components in PV systems. We describe the study design and data collection process for developing predictive models based on indoor laboratory testing data, which are then extended to outdoor field conditions with time-varying environmental variables, with prediction uncertainty quantified through simulation. Deep learning (DL) methods are also explored, and results are compared across modeling approaches. The parametric statistical model demonstrates good fit and predictive performance across datasets and shows greater robustness by incorporating physical and chemical knowledge. The DL model provides flexibility in capturing complex covariate relationships and often yields accurate predictions, though it is less robust across datasets. The paper concludes with remarks on key findings and their implications for PV reliability.
Comments32 pages, 13 figures
