基于SimCLR和可视化颜色V-I轨迹的非侵入性负载监控模型
Tie Chen1,2, Youyuan Fan1,2, Liping Li1,2
1College of Electrical and New Energy, China Three Gorges University, Yichang 443002, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究引入了一种非侵入性负载监控 (NILM) 的新框架,该框架使用可视化负载数据和对比学习. 它有效地解决了域移动问题,提高了负载识别准确性,但数据有限.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 目前的非侵入性负载监控 (NILM) 方法需要广泛的标记数据.
- 对于NILM的深度学习模型,它与域移动问题作斗争,限制了现实世界的应用.
研究的目的:
- 为NILM开发一个新的框架,克服数据依赖和领域转移的挑战.
- 在实际场景中提高负载识别的准确性和稳定性.
主要方法:
- 提出了一个SimCLR-ADA-LM框架,利用从未标记的源和目标域数据中可视化的颜色V-I轨迹.
- 采用对比式学习来进行类内聚合,并采用对抗式培训来进行跨领域的特征对齐.
- 微调模型以最小标记的目标域数据进行负载识别.
主要成果:
- 与传统方法相比,拟议的模型在跨领域识别任务中表现出优异的性能.
- 实现了显著的学习效率和识别稳定性,特别是在有限的标记数据下.
- 通过切除和比较实验结果进行验证.
结论:
- 该SimCLR-ADA-LM框架有效地解决了当前NILM方法的局限性.
- 该方法显示了实际NILM应用程序的巨大潜力,这些应用程序需要更少的标记数据和处理域变异.
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