A Bayesian inference approach to extract circuit model parameters and analyze photovoltaic degradation from power production data

Abstract

Power production data can be a valuable resource to analyze photovoltaic (PV) performance without the need for field surveys. Recent work has demonstrated the exciting possibility of leveraging this data to extract circuit model parameters and current-voltage properties of a PV system, but further development is needed to bolster these initial findings. Here, instead of using a classical optimization approach, we switch to the Bayesian framework to solve this complex multi-solution problem. This allows us to construct probability distributions over the model parameters, get a comprehensive picture of the solution space, and quantify prediction uncertainty. As a result, we can define confidence intervals for the system’s electrical properties and consistently track their daily evolution. Our results are validated with laboratory measurements for five silicon and thin-film modules, and our scalable approach works with on-site as well as online weather data, which opens new prospects for remote PV monitoring, modeling, and degradation analysis for real-life applications.