Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at Scale

Photovoltaic (PV) power stations have become an integral component to the global sustainable energy landscape. Accurately monitoring and estimating the performance of PV systems is critical to their feasibility for power generation and as a financial asset. One of the most challenging problems is to understand and estimate the long-term Performance Loss Rate (PLR) for large fleets of PV inverters. This paper introduces a novel Spatio-Temporal Graph Neural Network empowered, long-term Trend analysis system (ST-GTrend), to estimate PLR of PV systems at fleet-level. ST-GTrend nontrivially 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, with a design that can easily scale to large PV sets with effective, multi-level parallel computation. (1) To cope with diverse degradation patterns in timeseries, ST-GTrend adopts a paralleled graph autoencoder array to extract aging and fluctuation terms simultaneously, and imposes flatness and smoothness regularizations to disentangle between aging and fluctuation. (2) For large PV systems, ST-GTrend enables a multi-level parallelization paradigm to scale the training and inference computation with a provable performance guarantee. ST-GTrend has been deployed in CRADLE, a scientific high performance computing infrastructure. We evaluated ST-GTrend with three real-world large-scale PV datasets, spanning a time period of 10 years. Our results show that ST-GTrend reduces MAPE and Euclidean distance-based errors on average by 34.74% and 33.66% of SOTA methods, and scales well to large PV sets. We also showcase that the advantages of ST-GTrend generalize for the need of long-term trend analysis in financial and economic data.