Generating Synthetic Net Load Data with Physics-informed Diffusion Model

• URL: http://arxiv.org/abs/2406.01913v1
• Date: Tue, 4 Jun 2024 02:50:19 GMT
• Title: Generating Synthetic Net Load Data with Physics-informed Diffusion Model
• Authors: Shaorong Zhang, Yuanbin Cheng, Nanpeng Yu,
• Abstract summary: A conditional denoising neural network is designed to jointly train the parameters of the transition kernel of the diffusion model. A comprehensive set of evaluation metrics is used to assess the accuracy and diversity of the generated synthetic net load data.
• Score: 0.8848340429852071
• License: http://creativecommons.org/licenses/by/4.0/
• 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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