Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house. Efficient and accurate load monitoring facilitates user profile establishment, intelligent household energy management, and peak load shifting. This is beneficial for both the end-users and utilities by improving the overall efficiency of a power distribution network. Existing approaches mainly focus on developing an individual model for each appliance. Those approaches typically rely on a large amount of household-labeled data which is hard to collect. In this paper, we propose a multi-appliance-task framework with a training-efficient sample augmentation (SA) scheme that boosts the disaggregation performance with limited labeled data. For each appliance, we develop a shared-hierarchical split structure for its regression and classification tasks. In addition, we also propose a two-dimensional attention mechanism in order to capture spatio-temporal correlations among all appliances. With only one-day training data and limited appliance operation profiles, the proposed SA algorithm can achieve comparable test performance to the case of training with the full dataset. Finally, simulation results show that our proposed approach features a significantly improved performance over many baseline models. The relative errors can be reduced by more than 50\% on average. The codes of this work are available at https://github.com/jxiong22/MATNilm

翻译：非侵入式负荷监测（NILM）通过分解整个房屋的总用电信号，识别各类家用电器的运行状态和功耗。高效精准的负荷监测有助于建立用户画像、实现智能家居能源管理以及负荷峰值转移，这对于终端用户和电力公司均有益处，可提升配电网整体运行效率。现有方法主要针对每种电器开发独立模型，且通常依赖大量难以收集的家庭标注数据。本文提出一种多电器任务框架，结合训练高效的样本增强（SA）方案，在有限标注数据下提升分解性能。针对每种电器，我们为其回归与分类任务构建了共享分层拆分结构。此外，我们还提出了一种二维注意力机制，以捕获所有电器间的时空相关性。仅需一天的训练数据和有限的电器运行模式，所提出的SA算法即可达到与使用完整数据集训练相当的测试性能。仿真结果表明，与多种基线模型相比，本方法性能显著提升，相对误差平均降低超过50%。本工作代码见 https://github.com/jxiong22/MATNilm