Generating Synthetic Net Load Data with Physics-informed Diffusion Model

TL;DR

This paper introduces a physics-informed diffusion model for generating synthetic net load data, effectively addressing data scarcity and privacy issues while outperforming existing generative models in accuracy and diversity.

Contribution

The paper presents a novel physics-informed diffusion framework that integrates physical models into neural networks for improved synthetic data generation.

Findings

Outperforms state-of-the-art models by at least 20% across all metrics.

Successfully validated with real-world smart meter data.

Demonstrates versatility and generalizability to unforeseen scenarios.

Abstract

This paper presents a novel physics-informed diffusion model for generating synthetic net load data, addressing the challenges of data scarcity and privacy concerns. The proposed framework embeds physical models within denoising networks, offering a versatile approach that can be readily generalized to unforeseen scenarios. A conditional denoising neural network is designed to jointly train the parameters of the transition kernel of the diffusion model and the parameters of the physics-informed function. Utilizing the real-world smart meter data from Pecan Street, we validate the proposed method and conduct a thorough numerical study comparing its performance with state-of-the-art generative models, including generative adversarial networks, variational autoencoders, normalizing flows, and a well calibrated baseline diffusion model. A comprehensive set of evaluation metrics is used to assess the accuracy and diversity of the generated synthetic net load data. The numerical study results demonstrate that the proposed physics-informed diffusion model outperforms state-of-the-art models across all quantitative metrics, yielding at least 20% improvement.