| 论文标题： | |
|---|---|
| 作者： | |
| 出版刊物： | |
| 出版日期： | |
| 出版年份： | |
| 卷/期： | |
| DOI： | |
| 论文摘要： | The widespread adoption of advanced metering infrastructure has provided abundant data, enabling the integration of deep learning techniques into smart grids. However, it has also led to more sophisticated and concealed methods of electricity theft. Due to the challenges posed by data imbalance and missing values caused by device malfunctions and communication issues, existing deep learning models often perform poorly. To address these issues, this paper proposes a multi-step training framework named DING, which incorporates diffusion generation, self-supervised pre-training, normalized condition imputation, and generation-balanced fine-tuning. First, sufficient balanced smart meter data is generated using a diffusion model. Second, a pre-trained encoder is trained on the generated data, extracting unbiased low-dimensional features that can be used for downstream classification tasks and as conditions to guide the training of the imputation model. Next, an imputation model is trained based on a diffusion state-space equation. Finally, fine-tuning is performed on the balanced data. Experiments on a real dataset from the State Grid Corporation of China demonstrate that the proposed method outperforms previous models for both electricity theft detection and imputation tasks. |
以下内容与「星空体育彩票官网入口」同属公开资讯，可按栏目接着查阅相关条目。
本站按公开材料组织页面。需要原文时请核对应栏目发布页。
若从搜索引擎进入，可先确认当前栏目名称，再按需打开相关阅读。
