| 朱迪,赵杨阳,闫林芳,肖姿林,周咏,朱星阳.基于多尺度特征和注意力机制的非侵入式负荷识别[J].电力需求侧管理,2026,28(4):52-58 |
| 基于多尺度特征和注意力机制的非侵入式负荷识别 |
| Non-intrusive load identification based on multi-scale features and attention mechanism |
| 投稿时间：2026-01-06 修订日期：2026-03-16 |
| DOI：10.3969/j.issn.1009-1831.2026.04.008 |
| 中文关键词: 非侵入式负荷监测 家庭负荷识别 时序数据依赖关系 多尺度特征 注意力机制 |
| 英文关键词: non-invasive load monitoring household load identification time series data dependency relationship multi-scale features attention mechanism |
| 基金项目:国网江苏省电力有限公司科技项目(CEKY24063) |
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| 中文摘要: |
| 非侵入式负荷识别通过分析家庭或企业的总电力消耗数据,分解出各个电器的用电情况实现对用电行为的精确监测,对于节约能源、降低电力成本、推动智能电网具有重要意义。当前负荷识别方法在处理复杂时序数据时,难以捕捉数据长短期依赖关系的尺度多样性,导致识别准确度受限。针对以上问题,对时序数据的多尺度特征进行研究,通过一种多尺度特征提取策略,有效捕捉数据中不同时间尺度下的信息;同时,在提取的多尺度特征中引入潜在的注意力机制,使模型在关注数据中的关键时刻和重要特征的同时,降低了计算复杂度;最后,在公开数据集上进行实验,结果表明,相对其他对比方法,所提模型获得了最佳效果,验证了模型的有效性。 |
| 英文摘要: |
| Non-intrusive load identification decomposes the power usage of each appliance to achieve precise monitoring of appliance behavior by analyzing the total power consumption data of a household or enterprise. It is of great significance for energy conservation, reducing power costs, and promoting smart grids. When the current load identification methods deal with complex time series data, it is difficult to capture the scale diversity of the long-term and short-term dependencies of the data, resulting in limited identification accuracy. In response to the above problems, the multi-scale features of time series data are studied. Through a multi-scale feature extraction strategy, the information at different time scales in the data is effectively captured. Meanwhile, the potential attention mechanism is introduced into the extracted multi-scale features, enabling the model to focus on the key moments and important features in the data while reducing the computational complexity. Finally, experiments are conducted on the public dataset. The results show that, compared with other comparison methods, the proposed model achieves the best effect, verifying the validity of the model. |
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