Salvato in:
| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2404.08175 |
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Sommario:
- 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.