Engineering and deploying a hardware and software platform to collect and label non-intrusive load monitoring datasets
Engineering and deploying a hardware and software platform to collect and label non-intrusive load monitoring datasets
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设计和部署硬件和软件平台来收集和标记非侵入式负载监控数据集
DOI：
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发表时间：
2017
期刊：
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通讯作者：
N. Nunes
中科院分区：
文献类型：
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作者：
Lucas Pereira;M. Ribeiro;N. Nunes
目前用于收集和标记非侵入式负载监视（NILM）数据集的方法仍然严重依赖于对整个数据集进行冗长且容易出错的手动检查。因此，仍然很难找到完全标记的数据集，可以帮助进一步推动这一领域的研究。为了克服这种情况，我们提出了一个硬件和软件平台，以半自动标记的方式收集和标记NILM传感器数据。我们的平台结合了聚合和插件级智能仪表来测量消耗数据，软件算法可以自动检测不同监控负载的变化，以及最终用户可以监督标签过程的图形用户界面。在本文中，我们描述了组成我们平台的不同组件。我们还提出了一个现场部署的结果，进行测试我们的方法的可行性。部署的结果表明，我们的系统能够解释约82%的总负载，并自动检测94%的电源转换的插件级负载。
Current approaches for collecting and labeling NonIntrusive Load Monitoring (NILM) datasets still rely heavily on a lengthy and error prone manual inspection of the whole dataset. Consequently, it is still difficult to find fully labeled datasets that could help furthering even more the research in this field. In an attempt to overcome this situation, we propose a hardware and software platform to collect and label NILM sensor data in a semi-automatic labeling fashion. Our platform combines aggregate and plug-level smart-meters to measure consumption data, software algorithms to automatically detect changes in the different monitored loads and a graphical user interface where the end-user can supervise the labeling process. In this paper, we describe the different components that comprise our platform. We also present the results of one live deployment that was performed to test the feasibility of our approach. The results of the deployment show that our system was capable of explaining about 82% of the aggregate load, and automatically detect 94% of the power transitions in the plug-level loads.