EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Models
This addresses privacy and data scarcity issues for energy system operators and planners, though it is incremental as it adapts existing diffusion models to a specific domain.
The paper tackles the problem of generating high-resolution synthetic energy time series data, which is often restricted due to privacy concerns, by proposing EnergyDiff, a diffusion-based framework that improves temporal dependencies and marginal distributions, with significant gains at 1-minute resolution.
High-resolution time series data are crucial for the operation and planning of energy systems such as electrical power systems and heating systems. Such data often cannot be shared due to privacy concerns, necessitating the use of synthetic data. However, high-resolution time series data is difficult to model due to its inherent high dimensionality and complex temporal dependencies. Leveraging the recent development of generative AI, especially diffusion models, 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 the temporal dependencies and marginal distributions compared to baselines, particularly at the 1-minute resolution. EnergyDiff's universality is validated across diverse energy domains (e.g., electricity demand, heat pump, PV, multiple time resolutions (1 minute, 15 minutes, 30 minutes and 1 hour), and at both customer and transformer levels.