The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. We adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.

中文翻译：

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使用特定于设备的网络进行非侵入式设备识别

非侵入式负载监控（NILM）问题通常被表述为单通道盲源分离任务，其成功的解决方案可以实现快速方便的负载识别和能量分解。在测试时应用时，NILM 算法旨在从整个房屋的总功率测量中识别单个电器的运行特性。深度学习的最新进展产生了许多方法，这些方法主要侧重于学习从聚合测量到单个设备功率的直接映射。然而，这些方法不仅计算成本高，而且经常出现过拟合并且不能很好地泛化。在本文中，我们提出了一种新颖的 NILM 方法，该方法利用了统计学习方面的进步，而这些进步以前并未在该领域得到正确应用。所提出的方法包括三个阶段：首先，一种基于贝叶斯非参数学习的器具状态提取方法；第二，用于数据增强和减轻切换事件严重不平衡的合成少数过采样技术；第三，特定于设备的轻量级长短期记忆网络，用于每个设备的状态分类。我们采用“差分”输入（切换事件前后的差异）来降低网络训练的复杂性，并使所提出​​的方法对多设备切换事件具有鲁棒性。进行实验以证明所提出方法的有效性，与最近的方法相比，实现了优越的性能。进行消融研究以证明我们方法的每个模块的有效性。最后，