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Updated: May 28, 2025

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基于时间增强的多维特征可视化的非侵入性负载监控
Tie Chen1,2, Yimin Yuan3,4, Jiaqi Gao1,2
1Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang, 443002, China.
Scientific reports
|February 8, 2025
概括
本研究引入了一种新的非侵入性负载监控 (NILM) 方法,使用时间增强的V-I轨迹和ECA-ResNet34来准确识别家庭负载. 这种方法显著提高了能量分解的准确性,即使对于类似的负载.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 能源系统 能源系统
背景情况:
- 非侵入性负载监测 (NILM) 经常忽视时间V-I轨迹特征.
- 单一特征识别很难区分类似的负载.
研究的目的:
- 提出一种结合时间增强多维特征可视化的NILM方法.
- 提高家庭负载识别的准确性和通用性.
主要方法:
- 通过添加一个时间轴,开发了一个三维时空色彩V-I轨迹.
- 集成的电压/电流变化速率,功率因子和第三波.
- 使用ECA-ResNet34网络进行负载识别,减轻CNN退化问题.
主要成果:
- 在PLAID数据集上获得了97.3%的F1平均得分.
- 通过在WHITED数据集上使用转移学习,对类似的V-I轨迹和多个状态进行有效的负载识别.
结论:
- 提议的时间增强可视化和ECA-ResNet34模型提供了高效和准确的NILM.
- 转移学习增强了对不同数据集和负载类型的模型通用性和概括性.
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