为NILM捕获高频波符号:构建负载分类数据集
Farid Dinar1, Sébastien Paris1, Éric Busvelle1,2
1Laboratoire d'Informatique et des Systèmes (LIS), Unité Mixte de Recherche, Centre National de la Recherche Scientifique (UMR, CNRS) 7020, Université de Toulon, Aix Marseille Université, 83130 La Garde, France.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究引入了一种新的,具有成本效益的高频能源监控系统,以改进非侵入性负载监控 (NILM). 使用高频数据的机器学习显著提高了设备分类的准确性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 传统的非侵入性负载监控 (NILM) 难以处理低频数据,限制了设备分类的准确性.
- 高频电数据提供了更丰富的签名,包括律顺序,以提高NILM的性能.
- 缺乏全面的,可访问的公共数据集阻碍了先进的NILM研究.
研究的目的:
- 开发一个可扩展,具有成本效益的能源监测系统,用于生成详细的NILM数据集.
- 创建一个可复制和可访问的总和单个设备测量的数据集.
- 通过机器学习验证高频特征在提高NILM分解精度方面的有效性.
主要方法:
- 设计并实施了一种用于高频数据采集的新能源监测系统.
- 收集总和单个电器电气数据,形成一个新的NILM数据集.
- 应用机器学习技术来分析高频电气特征以进行设备分类.
主要成果:
- 开发的系统提供了详细的高频电气测量.
- 新的数据集使得可重复的NILM验证和研究成为可能.
- 使用高频特征的机器学习模型显示,与传统方法相比,分类精度更高.
结论:
- 高频数据采集系统对于推进NILM研究至关重要.
- 开发的数据集和方法解决了能源分类中的一个关键差距.
- 这项工作为改进实时能源分类和智能能源管理应用铺平了道路.
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