Fréchet 功率场景距离：一种用于评估智能电网中跨多时间尺度生成式AI模型的度量
Fréchet Power-Scenario Distance: A Metric for Evaluating Generative AI Models across Multiple Time-Scales in Smart Grids
AI总结：
本文提出一种基于Fréchet距离的新度量，用于从分布角度评估智能电网中生成式AI模型跨多时间尺度的合成数据质量，实证证明其优于传统欧氏距离度量。
摘要：
近年来，智能电网中的生成式人工智能（AI）模型取得了显著进展，这得益于它们能够生成大量合成数据，而由于保密限制，这些数据在现实世界中难以获取。利用此类合成数据的一个关键挑战是如何评估这些生成模型所产生数据的质量。传统的基于欧氏距离的度量仅反映两个单独样本之间的成对关系，可能无法评估合成数据集组之间的质量差异。在本工作中，我们提出了一种基于Fréchet距离（FD）的新度量，该距离在两个数据集之间于学习到的特征空间中估计。所提出的方法从分布角度评估生成质量。实证结果表明，所提出的度量在多个时间尺度和模型上具有优越性，增强了智能电网运行中数据驱动决策的可靠性。
英文摘要：
Generative artificial intelligence (AI) models in smart grids have advanced significantly in recent years due to their ability to generate large amounts of synthetic data, which would otherwise be difficult to obtain in the real world due to confidentiality constraints. A key challenge in utilizing such synthetic data is how to assess the data quality produced from such generative models. Traditional Euclidean distance-based metrics only reflect pair-wise relations between two individual samples, and could fail in evaluating quality differences between groups of synthetic datasets. In this work, we propose a novel metric based on the Fréchet Distance (FD) estimated between two datasets in a learned feature space. The proposed method evaluates the quality of generation from a distributional perspective. Empirical results demonstrate the superiority of the proposed metric across timescales and models, enhancing the reliability of data-driven decision-making in smart grid operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
