not-yet-known not-yet-known not-yet-known unknown Outdoor data is essential to study the reliability of PV modules and systems. Each electrical performance measure is dependent on the conditions the measurement is conducted at and, therefore, needs to be considered in the context of dynamically changing outdoor conditions. In this paper, we introduce a statistical model designed to analyze PV outdoor data. This model uses a timeseries of current-voltage ( IV) characteristics, alongside meteorological data, including plane-of-array irradiance ( G POA ) and module temperature ( T Mod ). The model aims to utilize all available information to predict the respective performance measure as well as its uncertainty at arbitrary conditions and times. First, to ensure its quality and relevance, a suitable filtering approach is applied to the IV curves, G POA and T Mod data from 9 modules from 5 locations (Arizona USA, Germany, India, Italy, Saudi Arabia) observed for over two years. Following this, we utilize the Extended Solar cell Parameters (ESPs), a descriptive model for IV characteristics using 10 parameters. The ESPs, then, undergo a principal component analysis (PCA), which transforms the EPSs into a set of uncorrelated principal components (PCs). Individual Gaussian process regressions (GPRs) are then trained on these principal components (PCs). Once the GPRs are trained, the model is capable of reproducing and predicting the complete IV characteristics at any given time t, for specified values of G POA and T Mod . This prediction includes an assessment of its standard deviation, which is derived from data noise and the distance from the observations. This model serves as a versatile tool for various applications, such as analyzing acclimatization effects, degradation trends, seasonal variations, and the performance ratio (PR) of PV modules or systems.

This study uncovers the impact of geographic variability on the distribution of sunlight on vehicle surfaces, focusing on two distinct scenarios: the flat-surface cargo area of a small delivery truck and the entire body of a commercial passenger vehicle. We employ a commuter pattern driving profile in Germany and a broader analysis incorporating random sampling of various road types and locations across Europe and the USA using LIDAR-derived topography and OpenStreetMap data. Our findings reveal significant irradiation inhomogeneity patterns shaped by the geographic landscape, road configurations, urban planning, and vegetation. The research identifies topography as the primary factor affecting irradiation distribution uniformity, with the vehicle’s surface orientation and curvature serving as secondary influencers. These insights hold immense potential for improving the design and efficiency of vehicle-integrated photovoltaic systems, presenting a foundational knowledge base from which to optimize energy capture in diverse environmental conditions.