用于大规模光伏退化分析的并行友好时空图学习
Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at Scale
AI总结：
提出ST-GTrend方法，利用时空图注意力与并行自编码器阵列分离光伏系统的长期老化与波动，并设计Para-GTrend加速算法，在10年数据集上使误差降低34.74%，最高加速7.92倍。
摘要：
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）电力网络进行机队级别的性能退化分析。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。准确估计光伏系统的性能，对于其作为发电技术和金融资产的可行性至关重要。在评估光伏系统的平准化度电成本（LCOE）时，最具挑战性的问题之一是理解和估计大型光伏逆变器机队的长期性能损失率（PLR）。ST-GTrend集成了时空相干性和图注意力机制，将PLR作为长期“老化”趋势从光伏输入数据的多个波动项中分离出来。为了应对时间序列中多样的退化模式，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施加平坦度和平滑度正则化，以确保老化与波动之间的解耦。为了将分析扩展到大型光伏系统，我们还引入了Para-GTrend，这是一种用于加速ST-GTrend训练和推理的并行算法。我们在三个跨越10年时间跨度的大规模光伏数据集上评估了ST-GTrend。结果显示，与SOTA方法相比，ST-GTrend将平均绝对百分比误差（MAPE）和欧几里得距离分别降低了34.74%和33.66%。结果证明，Para-GTrend能将ST-GTrend加速高达7.92倍。我们进一步使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。
英文摘要：
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.
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