Non Intrusive Load Monitoring (NILM), also called, load disaggregation aims to infer load level electrical energy consumption from the aggregate power signal. Several challenges are limiting the deployment of NILM systems in residential and commercial buildings. In this paper, we treat the case of energy disaggregation in the presence of non-targeted loads. Non-targeted loads in our context stand for electrical loads for which we do not have labels during the training phase of the NILM algorithm. However, those loads may exist in a real-world scenario, and their power consumption adds to the aggregate power signal. In this work, we present our load disaggregation method based on a multi-label classification approach and study the impact of non-targeted loads on the NILM disaggregation performance. We show that the non-targeted loads can negatively affect the disaggregation performance of NILM systems and found a significant correlation between the disaggregation performance impact and the overlapping coefficient of the targeted and non targeted loads’ power distributions. Results are obtained using a publicly available dataset of power measurements.

非侵入式负载监控 (NILM)，也称为负载分解，旨在从总功率信号推断负载级别的电能消耗。一些挑战限制了 NILM 系统在住宅和商业建筑中的部署。在本文中，我们处理存在非目标载荷时能量分解的情况。我们上下文中的非目标负载代表在 NILM 算法的训练阶段我们没有标签的电力负载。然而，这些负载可能存在于现实场景中，它们的功耗会增加总功率信号。在这项工作中，我们提出了基于多标签分类方法的负载分解方法，并研究了非目标负载对 NILM 分解性能的影响。我们表明非目标负载会对 NILM 系统的分解性能产生负面影响，并发现分解性能影响与目标和非目标负载功率分布的重叠系数之间存在显着相关性。结果是使用公开可用的功率测量数据集获得的。