Generating Synthetic Net Load Data with Physics-informed Diffusion Model
<|reference_start|>Generating Synthetic Net Load Data with Physics-informed Diffusion Model: 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.<|reference_end|>
@article{zhang2024generating,
    title={Generating Synthetic Net Load Data with Physics-informed Diffusion Model},
    author={Shaorong Zhang, Yuanbin Cheng, Nanpeng Yu},
    journal={arXiv preprint arXiv:2406.01913},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01913},
    primaryClass={cs.LG cs.AI}
}