一个基于SFRA技术和机器学习的新NILM系统
Simone Mari1, Giovanni Bucci1, Fabrizio Ciancetta1
1Dipartimento di Ingegneria Industriale e dell'Informazione e di Economia, Università dell'Aquila, 67100 L'Aquila, Italy.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种价格实惠,易于安装的非侵入性负载监控 (NILM) 系统,可以准确检测设备状态 (ON/OFF). 该方法使用扫描频率响应分析 (SFRA) 和支持矢量机 (SVM) 算法来实现可靠的能源管理.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 传统的非侵入性负载监控 (NILM) 系统主要关注能源消耗,而不是设备状态.
- 现有的NILM系统难以为单个负载提供实时开启/关闭状态,阻碍了现代能源管理.
- 精确的负载状态监控对于智能家居,能源效率和辅助生活环境至关重要.
研究的目的:
- 开发一种廉价且易于安装的监控系统,用于非侵入性的负载状态检测.
- 为了能够监控单个电荷状态 (ON/OFF),而不论其能耗如何.
- 为先进的家庭,能源和辅助环境管理系统提供必要的反.
主要方法:
- 拟议的系统处理通过扫描频率响应分析 (SFRA) 获得的测量痕迹.
- 使用支持矢量机 (SVM) 算法来分析SFRA数据并确定负载状态.
- 对各种电荷进行了广泛的测试,以验证该技术的有效性.
主要成果:
- 开发的系统在检测负载状态方面达到很高的准确性,从94%到99%不等.
- 准确性受到用于支持矢量机 (SVM) 模型的训练数据量的影响.
- 该系统在各种负载类型的众多测试中显示出积极的结果.
结论:
- 拟议的基于SFRA的系统为非侵入性负载状态监控提供了有效和负担得起的解决方案.
- 这项技术通过提供关键的开启/关闭信息来增强NILM系统的功能.
- 这些发现支持将该系统集成到智能能源管理和辅助生活应用中.
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