发表机构
Florida State University（佛罗里达州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SynEnergy，一种两阶段扩散框架，通过HG-ASL提取异常语义、AS-Diff生成数据，在四个真实能源数据集上较11种基线提升异常保留与下游质量，可扩展至城市级场景。
AI 中文摘要
细粒度能源消耗数据是需求预测、需求响应规划和电网可靠性评估等应用的关键基础。然而，受隐私问题和数据共享限制，这类数据的获取往往存在障碍，这使得合成能源数据生成的研究日益受到关注。现有方法虽能复现整体消耗分布和周期性时间模式，但常会平滑或低估由极端天气、基础设施故障和行为转变引发的异常事件。保留这些异常事件极具挑战性，因为它们稀疏、在时空上局部化，且受地理邻近性和区域属性的异构依赖关系影响。为应对这些挑战，本文提出SynEnergy，一种用于保留异常的能源消耗数据生成的两阶段扩散框架。第一阶段为基于异构图的异常语义学习（HG-ASL），通过联合建模城市区域间的空间和属性依赖关系，从稀疏残差结构中提取特定区域的异常语义；第二阶段为异常语义引导扩散（AS-Diff），将学习到的异常语义注入去噪过程，以生成真实的消耗序列并保留异常模式。该设计支持单个区域的可控生成，且可自然扩展至城市范围的场景。本文在四个真实能源消耗数据集上，将SynEnergy与11种通用及能源专用生成基线进行评估。实验结果表明，与基线相比，SynEnergy的异常保留保真度平均提升12.21%，下游质量提升2.96%，同时保持了具有竞争力的整体生成保真度。
英文摘要
Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment. However, access to such data is often restricted by privacy concerns and data-sharing constraints, motivating growing interest in synthetic energy data generation. Although existing methods can reproduce overall consumption distributions and recurring temporal patterns, they often smooth out or underrepresent anomalous events caused by extreme weather, infrastructure failures, and behavioral shifts. Preserving these events is challenging because they are sparse, localized in time and space, and shaped by heterogeneous dependencies across geographical proximity and regional attributes. To address these challenges, we propose SynEnergy, a two-stage diffusion-based framework for anomaly-preserving energy consumption data generation. The first stage, Heterogeneous Graph-based Anomaly Semantic Learning (HG-ASL), extracts region-specific anomaly semantics from sparse residual structures by jointly modeling spatial and attribute dependencies across urban regions. The second stage, Anomaly Semantic-guided Diffusion (AS-Diff), injects the learned anomaly semantics into the denoising process to generate realistic consumption sequences while preserving anomalous patterns. This design enables controllable generation for individual regions and scales naturally to city-wide settings. We evaluate SynEnergy on four real-world energy consumption datasets against 11 general-purpose and energy-specific generation baselines. Experimental results show that SynEnergy improves anomaly preservation fidelity by an average of 12.21% and downstream quality by 2.96%, while maintaining competitive overall generation fidelity compared to baselines.
Comments24 pages, 44 figures
