在"SmartNIALMeter"电器设备分类数据集"SmartNIALMeter"
Manuel Vogel1, Martin Friedli1, Martin Camenzind1
1Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.
Data in brief
|September 17, 2024
概括
本研究引入了用于电气分类 (NILM) 的大型数据集,以改善从聚合能源信号中识别设备. 这些数据支持开发新的NILM算法,用于能源管理和电网稳定.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 能源系统 能源系统
背景情况:
- 非侵入性负载监控 (NILM) 从总体信号中识别单个设备的能耗.
- 对于配电系统运营商 (DSO),NILM的应用包括用户能源反和电网负载转移.
研究的目的:
- 介绍SmartNIALMeter数据集,这是NILM研究的全面集合.
- 为了促进先进的NILM算法的开发和验证.
主要方法:
- 在两年内从20座建筑物收集了大量数据,每隔5秒抽取样本.
- 配备了100台配备专用传感器的仪器,用于分计量,以及用于总消耗的智能计量器数据.
- 包括各种负载,如热和电动汽车充电站,以及单相和三相设备.
主要成果:
- 该数据集包括各种建筑类型的众多电器的详细能源消耗数据.
- 它捕捉了现实世界的负载变化,这对于强大的NILM算法训练和测试至关重要.
- 为特定设备的能源使用提供基本真相.
结论:
- 智能NIALMeter数据集是推动NILM研究的宝贵资源.
- 能够对NILM算法进行调查,特别是那些需要有限的次计量数据的算法.
- 支持能源效率和智能电网管理方面的应用.
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