联合学习增强的生成模型用于智能家居中的非侵入性负载监控
Yuefeng Lu1,2, Shijin Xu3, Yadong Liu1
1Smart Grid Center, Shanghai Jiao Tong University, Shanghai, China.
Scientific reports
|July 29, 2025
概括
本研究介绍了与瓦斯斯坦生成对抗网络 (FL-WGAN) 进行非侵入性负载监控 (NILM) 的联合学习. 这种新的方法通过学习各种负载模式,提高智能电网效率,提高了能源分解的准确性.
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:
- 非侵入性负载监测 (NILM) 对于智能电网来说至关重要,它可以从汇总的家庭数据中估计个别设备的电力消耗.
- 对于NILM而言,现有的联合学习 (FL) 方法往往难以准确,因为生成模型专注于常见的负载模式,忽视更罕见的模式.
研究的目的:
- 在联合学习框架内提高NILM的准确性.
- 解决现有的生成模型在捕捉负载功率分布的全部频谱方面的局限性.
主要方法:
- 建议使用瓦斯斯坦生成对抗网络 (FL-WGAN) 架构进行联合学习.
- 每个客户训练一个生成网络,被一个歧视者评估,使用Wasserstein基于距离的指导机制.
- 将注意力机制集成到生成模型中,以提高代表性能力.
主要成果:
- 与现有的NILM技术相比,FL-WGAN方法显示出更高的性能.
- 使用UA-DALE和REDD数据集进行了评估,验证了拟议方法的有效性.
结论:
- FL-WGAN架构有效地提高了联合学习环境中的NILM准确性.
- 这种方法可以更全面地学习负载功率分布,从而实现更好的能源管理和防止浪费.
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