Understanding Degradation Patterns in Photovoltaic Systems through Bayesian Modeling

This study uses extensive operational data to analyze Bayesian approaches for predicting photovoltaic module degradation. It emphasizes the application of Bayesian statistics, which incorporates prior knowledge and uncertainty in modeling degradation. Key model selection criteria, such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), are highlighted for determining effective predictive models. The mechanical theory section discusses factors contributing to degradation, including thermal stress and environmental influences. Three levels of Bayesian models are introduced: Level 1 employs basic techniques, Level 2 uses advanced models like hierarchical Bayesian models, and Level 3 explores complex techniques such as Bayesian networks. A comparative analysis evaluates model performance using the Widely Applicable Information Criterion (WAIC), AIC, and BIC criteria. This study demonstrates that while linear models are often sufficient for capturing the degradation of photovoltaic systems, complex models are advantageous for addressing nonlinear degradation phenomena, ultimately enhancing predictive maintenance strategies for improved reliability and longevity.