  High-resolution time series data are crucial for operation and planning in
energy systems such as electrical power systems and heating systems. However,
due to data collection costs and privacy concerns, such data is often
unavailable or insufficient for downstream tasks. Data synthesis is a potential
solution for this data scarcity. With the recent development of generative AI,
we propose EnergyDiff, a universal data generation framework for energy time
series data. EnergyDiff builds on state-of-the-art denoising diffusion
probabilistic models, utilizing a proposed denoising network dedicated to
high-resolution time series data and introducing a novel Marginal Calibration
technique. Our extensive experimental results demonstrate that EnergyDiff
achieves significant improvement in capturing temporal dependencies and
marginal distributions compared to baselines, particularly at the 1-minute
resolution. Additionally, EnergyDiff consistently generates high-quality time
series data across diverse energy domains, time resolutions, and at both
customer and transformer levels with reduced computational need.
