家庭用电器的过渡数据集具有强烈的切换事件
Dongyang Zhang1,2, Xiaohu Zhang1, Lei Hua1
1Department of Computer Science North China Electric Power University (Baoding), BaoDing, China.
Scientific data
|May 14, 2024
概括
研究人员开发了用于非侵入性负载监控 (NILM) 的具有密集切换事件 (TDHA) 的家用电器过渡数据集. 这一数据集有助于提高与中国相似的电力使用模式的地区的设备识别精度.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 非侵入性负载监控 (NILM) 使用深度学习 (DL) 进行设备识别和能源分析.
- 由于电力环境的变化,NILM研究需要广泛的,特定于区域的数据集.
- 现有的数据集可能无法完全捕捉到不同区域电力消费模式的细微差别.
研究的目的:
- 介绍家用电器与强度切换事件 (TDHA) 数据集的短暂数据集.
- 为中国和类似地区的NILM研究提供高质量的开放数据集.
- 提高NILM算法的性能和适用性.
主要方法:
- 从10种常见的家用电器收集了瞬间开关数据.
- 确保高采样率和准确标记器件事件.
- 使用精确的继电器控制来切换设备,以最大限度地减少机械干扰.
主要成果:
- TDHA数据集提供了现实的设备启动波形.
- 具有精确的标签和高采样率,用于详细分析.
- 通过使用可控继电器开关来减轻干扰.
结论:
- 该TDHA数据集支持NILM算法的进步.
- 在NILM系统中提高了设备识别准确度.
- 允许在具有与中国相似的电力特征的地区更广泛地应用NILM.
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