Non-intrusive load monitoring is a technique to help power companies monitor and analyze residential energy usage. Aggregated power load measurements for a household (i.e., the signal on the main powerline) are disaggregated into individual appliance loads by examining the appliance-specific power consumption characteristics. These data can then be used to modify consumer behaviors via detailed billing and/or demand-pricing tariffs. A number of advances in the field have been reported in the past two decades, many of which apply machine learning algorithms. However, these algorithms usually only assign one label to an example, which is a poor match to the monitoring problem, meaning elaborate encodings or classifier ensembles are needed. A more elegant solution would be to use algorithms that assign multiple labels to a single example. These multi-label classification algorithms have received very little attention in this field to date. We conduct an experimental investigation of four multi-label classification algorithms for non-intrusive monitoring and find that the best one is superior to the existing reported results on multiple real-world household datasets.

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使用多标签分类方法的非侵入式负载监控

非侵入式负载监控是一种帮助电力公司监控和分析住宅能源使用情况的技术。通过检查特定于设备的功耗特性，将家庭的总功率负载测量值（即主电力线上的信号）分解为单个设备负载。然后可以使用这些数据通过详细的计费和/或需求定价关税来修改消费者行为。在过去的 20 年中，该领域取得了许多进展，其中许多应用了机器学习算法。然而，这些算法通常只为一个例子分配一个标签，这与监控问题的匹配度很差，这意味着需要复杂的编码或分类器集成。更优雅的解决方案是使用为单个示例分配多个标签的算法。迄今为止，这些多标签分类算法在该领域很少受到关注。我们对四种用于非侵入式监控的多标签分类算法进行了实验研究，发现最好的算法优于现有的多个真实家庭数据集报告的结果。