To predict the final output of a photovoltaic (PV) system, a variety of different types of models are required, like plane-of-array (POA) transposition, module temperature, and others depending on what meteorological data is available. As the data flows through the modeling pipeline, uncertainties can arise. As these uncertainties accumulate, it can be difficult to determine whether the issues are due to measurement or modeling errors. When multiple models are being used in an estimation, there is no way to directly attribute errors to any one model specifically. In effort to decrease these uncertainties, a standardized process for model validation was created for the most used models in the PV modeling pipeline.
This standardized process uses three different phases of analysis to gauge the model’s performance: basic error analysis (RMSE, MBE, etc), residual analysis, and baseline model comparison. This outline is demonstrated for transposition, module temperature, PV performance, incidence angle modifier (IAM), and decomposition models in the form of individual Jupyter Notebooks. Using a full year of hour-level field data, these notebooks allow for modelers to evaluate their models at a wide range of conditions to determine seasonal or time-of-day performance. The annual energy yield is calculated using both modeled and measured values to observe the direct impact of the model’s errors. The analysis also provides insight into which variables within the model could be negatively affecting the model’s output. Using a well-established and validated model of the same type, the user’s model is compared to the baseline model.
Following the process described above will allow modelers and model developers to improve their tools and will ultimately result in lower uncertainty and more consistent results.
Reference: L. Deville, K. S. Anderson, M. Theristis, “Towards a Reproducible Photovoltaic Modeling Validation Process,” 2024 IEEE 52nd Photovoltaic Specialists Conference (PVSC), Seattle, WA, USA, 2024