A Novel Vision Transformer based Load Profile Analysis using Load Images as Inputs
<|reference_start|>A Novel Vision Transformer based Load Profile Analysis using Load Images as Inputs: This paper introduces ViT4LPA, an innovative Vision Transformer (ViT) based approach for Load Profile Analysis (LPA). We transform time-series load profiles into load images. This allows us to leverage the ViT architecture, originally designed for image processing, as a pre-trained image encoder to uncover latent patterns within load data. ViT is pre-trained using an extensive load image dataset, comprising 1M load images derived from smart meter data collected over a two-year period from 2,000 residential users. The training methodology is self-supervised, masked image modeling, wherein masked load images are restored to reveal hidden relationships among image patches. The pre-trained ViT encoder is then applied to various downstream tasks, including the identification of electric vehicle (EV) charging loads and behind-the-meter solar photovoltaic (PV) systems and load disaggregation. Simulation results illustrate ViT4LPA's superior performance compared to existing neural network models in downstream tasks. Additionally, we conduct an in-depth analysis of the attention weights within the ViT4LPA model to gain insights into its information flow mechanisms.<|reference_end|>
@article{kim2024a,
    title={A Novel Vision Transformer based Load Profile Analysis using Load Images
  as Inputs},
    author={Hyeonjin Kim, Yi Hu, Kai Ye, Ning Lu},
    journal={arXiv preprint arXiv:2404.08175},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08175},
    primaryClass={eess.SY cs.SY}
}