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.

翻译：我们提出了一种新颖的基于时空图神经网络的趋势分析方法（ST-GTrend），用于对光伏（PV）电力网络进行机群级性能退化分析。光伏电站已成为全球可持续能源生产格局的重要组成部分。准确评估光伏系统的性能，对于其作为发电技术和金融资产的可行性至关重要。在评估光伏系统平准化能源成本（LCOE）时，最具挑战性的问题之一是理解并估计大型光伏逆变器机群的长期性能损失率（PLR）。ST-GTrend 整合了时空一致性与图注意力机制，将 PLR 作为长期“老化”趋势，从光伏输入数据中的多个波动项中分离出来。为了应对时间序列中的多样退化模式，ST-GTrend 采用并行图自编码器阵列，同时提取老化项和波动项。ST-GTrend 引入平坦度与平滑度正则化，以确保老化与波动之间的解耦。为将分析扩展至大型光伏系统，我们还提出了 Para-GTrend，这是一种并行算法，用于加速 ST-GTrend 的训练与推理。我们已在三个大规模光伏数据集上对 ST-GTrend 进行了评估，这些数据集覆盖了 10 年的时间跨度。结果表明，与最先进的方法相比，ST-GTrend 的平均绝对百分比误差（MAPE）和欧氏距离分别降低了 34.74% 和 33.66%。我们的结果还表明，Para-GTrend 可将 ST-GTrend 的速度提升至多 7.92 倍。我们进一步利用金融和经济数据集验证了 ST-GTrend 在趋势分析中的通用性和有效性。