Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic
  Degradation Analysis at Scale
Yangxin Fan Raymond Wieser Laura Bruckman Roger French Yinghui Wu
We propose a novel Spatio-Temporal Graph Neural Network empowered trend
analysis approach (ST-GTrend) to perform fleet-level performance degradation
analysis for Photovoltaic (PV) power networks. PV power stations have become an
integral component to the global sustainable energy production landscape.
Accurately estimating the performance of PV systems is critical to their
feasibility as a power generation technology and as a financial asset. One of
the most challenging problems in assessing the Levelized Cost of Energy (LCOE)
of a PV system is to understand and estimate the long-term Performance Loss
Rate (PLR) for large fleets of PV inverters. ST-GTrend integrates
spatio-temporal coherence and graph attention to separate PLR as a long-term
"aging" trend from multiple fluctuation terms in the PV input data. To cope
with diverse degradation patterns in timeseries, ST-GTrend adopts a paralleled
graph autoencoder array to extract aging and fluctuation terms simultaneously.
ST-GTrend imposes flatness and smoothness regularization to ensure the
disentanglement between aging and fluctuation. To scale the analysis to large
PV systems, we also introduce Para-GTrend, a parallel algorithm to accelerate
the training and inference of ST-GTrend. We have evaluated ST-GTrend on three
large-scale PV datasets, spanning a time period of 10 years. Our results show
that ST-GTrend reduces Mean Absolute Percent Error (MAPE) and Euclidean
Distances by 34.74% and 33.66% compared to the SOTA methods. Our results
demonstrate that Para-GTrend can speed up ST-GTrend by up to 7.92 times. We
further verify the generality and effectiveness of ST-GTrend for trend analysis
using financial and economic datasets.