公制(单位)
计算机科学
限制
能量(信号处理)
实时计算
能源消耗
可靠性工程
鉴定(生物学)
事件(粒子物理)
分解
采样(信号处理)
功率消耗
功率(物理)
数据挖掘
分布式计算
工程类
数学
电气工程
电信
统计
物理
生态学
探测器
生物
机械工程
量子力学
植物
运营管理
作者
X. H. Guo,Chao Wang,Tao Wu,Ruiheng Li,Houyi Zhu,Huaiqing Zhang
出处
期刊:Applied Energy
[Elsevier BV]
日期:2023-05-08
卷期号:343: 121193-121193
被引量:13
标识
DOI:10.1016/j.apenergy.2023.121193
摘要
Results from Non-Intrusive Load Monitoring serve for energy decomposition and load identification, which would facilitate effective energy consumption management. Existing studies have focused on settings with a fixed number of electrical appliances. This differs significantly from real-world scenarios, thus largely limiting the practical application of related research. We study the pattern variations of the aggregated power sequences and separately analyze two different scenarios for new appliance introduction. Based on this, we propose a novel appliance detection method that can be implemented for low-frequency sampling data. The proposed method can determine whether new appliances are introduced without the prior information at the appliance level, thus establishing a basis for load monitoring in scenarios with dynamic changes in load topology. The experimental results show that the proposed method achieves at least a 35.93 % improvement in the metric of F1 compared to the event-based approach in the presence of a different number of unknown appliances.
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