论文标题
一种基于事件的新型非侵入负载监测算法
A Novel Event-based Non-intrusive Load Monitoring Algorithm
论文作者
论文摘要
非侵入式负载监测（NILM）旨在通过纯粹的分析方法从聚合功率信号中推断设备的功率曲线。现有的NILM方法容易受到各种问题的影响，例如功率信号的噪声和瞬态尖峰，在模式过渡时间上超出冲动，通过不同的设备关闭消耗值以及不可用的大型培训数据集。本文提出了一种基于事件的新型NILM分类算法来缓解这些问题。拟议的算法（I）过滤器的功耗信号并准确检测到所有事件，（ii）提取设备的特定特征，例如操作模式及其各自的功耗间隔，从训练数据集中的功耗信号，以及（III）标签，具有高准确的标签。该算法使用REDD进行验证，结果显示其有效性可通过现有智能计量仪准确地分解低频测量数据。
Non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power consumption signals and accurately detects all events, (ii) extracts specific features of appliances, such as operation modes and their respective power consumption intervals, from their power consumption signals in the training dataset, and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low frequency measured data by existing smart meters.
