SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

2026-08-04

AI Summary

Existing synthetic energy data generation methods struggle to preserve sparse, spatiotemporally localized anomalies caused by extreme weather or infrastructure failures, thereby limiting downstream task performance. This work proposes a two-stage diffusion-based generative framework that first employs a heterogeneous graph neural network to model complex inter-regional dependencies and extract anomaly semantics, then injects this information into the diffusion denoising process to enable anomaly-aware, high-fidelity time series synthesis. To the best of our knowledge, this is the first approach to integrate heterogeneous graph modeling with diffusion models for controllable, scalable, and precise anomaly preservation. Evaluated on four real-world datasets, the method achieves an average 12.21% improvement in anomaly fidelity and a 2.96% gain in downstream task performance, while maintaining overall generation quality on par with state-of-the-art baselines.

Abstract

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.

Problem

• synthetic energy data
• anomaly preservation
• energy consumption
• privacy constraints
• spatiotemporal anomalies

Innovation

• anomaly preservation
• diffusion model
• heterogeneous graph
• synthetic energy data
• semantic-guided generation