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arXiv · 2406.01913

Generating Synthetic Net Load Data with Physics-informed Diffusion Model

Shaorong Zhang·Yuanbin Cheng·Nanpeng Yu
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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.

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Shaorong Zhang, Yuanbin Cheng, Nanpeng Yu. 2024-06-04. Generating Synthetic Net Load Data with Physics-informed Diffusion Model. https://arxiv.org/abs/2406.01913

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