论文标题
基于设备电气使用的机器学习方法非侵入性房屋缺席检测
Machine Learning Approaches for Non-Intrusive Home Absence Detection Based on Appliance Electrical Use
论文作者
论文摘要
家庭缺席检测是智能家庭装置上的新兴领域。在许多情况下，确定房屋的居民是否存在很重要。可能的情况包括但不限于：一个独自生活的老年人，患有痴呆症的人，家庭隔离。大多数已发表的论文都集中在压力 /门传感器或摄像机上，以检测郊外事件。尽管上述方法可提供稳固的结果，但它们是侵入性的，需要修改传感器放置。在我们的工作中，将设备电气使用作为检测居民存在或不存在的手段。能源使用是功率分解的结果，这是一种非侵入性 /非侵入性传感方法。由于无法提供提供能源数据和地面真相的数据集，因此在英国 - 销售数据集上引入了人工郊游事件，这是一个著名的非侵入性负载监控数据集（NILM）。使用生成的数据集评估了几种机器学习算法。基准结果表明，使用设备功耗的家庭缺席检测是可行的。
Home absence detection is an emerging field on smart home installations. Identifying whether or not the residents of the house are present, is important in numerous scenarios. Possible scenarios include but are not limited to: elderly people living alone, people suffering from dementia, home quarantine. The majority of published papers focus on either pressure / door sensors or cameras in order to detect outing events. Although the aforementioned approaches provide solid results, they are intrusive and require modifications for sensor placement. In our work, appliance electrical use is investigated as a means for detecting the presence or absence of residents. The energy use is the result of power disaggregation, a non intrusive / non invasive sensing method. Since a dataset providing energy data and ground truth for home absence is not available, artificial outing events were introduced on the UK-DALE dataset, a well known dataset for Non Intrusive Load Monitoring (NILM). Several machine learning algorithms were evaluated using the generated dataset. Benchmark results have shown that home absence detection using appliance power consumption is feasible.
