A Gaussian process regression IV model for PV outdoor data
- Timon S. Vaas,
- Bart Pieters,
- Evgenii Sovetkin,
- Andreas Gerber,
- Uwe Rau
Timon S. Vaas
Forschungszentrum Julich GmbH
Corresponding Author:t.vaas@fz-juelich.de
Author ProfileAbstract
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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.