提出新度量方法，评估智能电网生成数据在多时间尺度下的质量。
Fréchet Power-Scenario Distance: A Metric for Evaluating Generative AI Models across Multiple Time-Scales in Smart Grids
- 基于特征空间中的弗雷歇距离，从分布角度评估生成数据质量。
- 在多时间尺度和不同模型上均优于传统欧氏距离方法。
- 适合电力系统领域研究人员评估生成数据可靠性。
近年来，生成式人工智能在智能电网中取得显著进展，因其能生成大量难以通过真实世界获取的合成数据，尤其在保密性限制下更具优势。然而，如何评估生成数据的质量成为关键挑战。传统基于欧氏距离的度量仅反映单个样本间的成对关系，在评估合成数据集间质量差异时表现不佳。本文提出一种基于弗雷歇距离（Fréchet Distance）的新度量方法，该方法在学习的特征空间中估计两个数据集之间的距离，从分布视角评估生成质量。实证结果表明，该方法在多时间尺度和多种生成模型下均表现出优越性能，提升了智能电网数据驱动决策的可靠性。
原文摘要 · Abstract (English)
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.
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