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.

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