Semi-Automatic Generation and Labeling of Training Data for Non-intrusive Load Monitoring
Semi-Automatic Generation and Labeling of Training Data for Non-intrusive Load Monitoring
复制标题
半自动生成和标记用于非侵入式负载监控的训练数据
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
B. Becker
中科院分区：
文献类型：
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作者：
Benjamin Völker;P. Scholl;B. Becker
用户意识是减少家庭不必要能源消耗的主要驱动力之一。然而，这种意识需要我们拥有的设备的个人能量数据。获取此数据的一种改进方法是使用非侵入式负载监视方法。这些方法大多是有监督的，需要提前收集标记的地面真实数据。标记设备的接通阶段已经是一个繁琐的过程，但是如果需要关于内部设备状态的进一步信息（例如HVAC的强度），则手动标记方法是不可行的。我们提出了一种新的非侵入式负载监测的数据收集和标记方法。这种方法使用直接连接到被监控设备的侵入式传感器。后处理步骤将连接的设备分为四类，并以半自动的方式显示内部状态序列。我们使用一个样本数据集评估了我们的标记方法，该样本数据集比较了识别的事件、状态和分类器械类别的数量。事件检测器对于在其功率信号中显示不同状态的设备实现了86.52%的总F1分数。使用我们的框架，整体标签工作减少了一半以上（42%）。
User awareness is one of the main drivers to reduce unnecessary energy consumption in our homes. This awareness, however, requires individual energy data of the devices we own. A retrofittable way to get this data is to use Non-Intrusive Load Monitoring methods. Most of these methods are supervised and require to collect labeled ground truth data in advance. Labeling on-phases of devices is already a tedious process, but if further information about internal device states are required (e.g. intensity of an HVAC), manual labeling methods are infeasible. We propose a novel data collection and labeling method for Non-Intrusive Load Monitoring. This method uses intrusive sensors directly connected to the monitored devices. A post-processing step classifies the connected devices into four categories and exposes internal state sequences in a semi-automatic way. We evaluated our labeling method with a sample dataset comparing the amount of recognized events, states and classified device category. The event detector achieved a total F1 score of 86.52 % for devices which show distinct states in its power signal. Using our framework, the overall labeling effort is cut by more than half (42%).
基于声学的设备状态识别，用于细粒度能源分析
DOI：
10.1109/percom.2015.7146510
发表时间：
2015
期刊：
2015 IEEE International Conference on Pervasive Computing and Communications (PerCom
影响因子：
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作者：
Pathak, Nilavra;Khan, Md Abdullah;Roy, Nirmalya
通讯作者：
Roy, Nirmalya