EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Models

2024-07-18

arXiv.org

AI Summary

High-resolution temporal energy data (e.g., minute-level electricity/heat load) is difficult to share across stakeholders due to stringent privacy constraints. Method: This paper proposes a synthetic data generation framework based on Denoising Diffusion Probabilistic Models (DDPMs), featuring a dedicated temporal U-Net denoising architecture and a novel marginal calibration loss to enable end-to-end training. Contribution/Results: To the best of our knowledge, this is the first approach supporting cross-energy-type, multi-spatial-scale (from individual users to transformer-level), and high-resolution (minute-level) generation while preserving distributional fidelity. Experiments demonstrate that our method significantly outperforms GAN- and LSTM-based baselines at 1-minute granularity, accurately capturing long-range temporal dependencies and marginal distributions. Generated samples exhibit superior statistical quality and lower computational overhead compared to existing alternatives.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Web data generation and simulation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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. Additionally, EnergyDiff consistently generates highquality time series data across diverse energy domains, time resolutions, and at both customer and transformer levels with reduced computational need.

Problem

• High-resolution Energy Data
• Privacy Protection
• Energy System Management

Innovation

• EnergyDiff
• DiffusionModels
• PrivacyProtection