Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combines a regression subnetwork with a classification subnetwork for solving the NILM problem. Specifically, we improve the generalization capability of the overall architecture by including an encoder–decoder with a tailored attention mechanism in the regression subnetwork. The attention mechanism is inspired by the temporal attention that has been successfully applied in neural machine translation, text summarization, and speech recognition. The experiments conducted on two publicly available datasets—REDD and UK-DALE—show that our proposed deep neural network outperforms the state-of-the-art in all the considered experimental conditions. We also show that modeling attention translates into the network’s ability to correctly detect the turning on or off an appliance and to locate signal sections with high power consumption, which are of extreme interest in the field of energy disaggregation。
翻译参考：在文献中称为非侵入式负载监控 (NILM) 的能量分解是在给定监控多个设备的单个智能电表记录的总功率需求的情况下推断单个设备的功率需求的任务。在本文中，我们提出了一种将回归子网络与分类子网络相结合的深度神经网络，用于解决 NILM 问题。具体来说，我们通过在回归子网络中包含一个具有定制注意机制的编码器 - 解码器来提高整体架构的泛化能力。注意机制的灵感来自已成功应用于神经机器翻译、文本摘要和语音识别的时间注意。在两个公开可用的数据集 REDD 和 UK-DALE 上进行的实验表明，我们提出的深度神经网络在所有考虑的实验条件下都优于最先进的网络。我们还表明，建模注意力转化为网络正确检测打开或关闭电器并定位高功耗信号部分的能力，这在能量分解领域非常重要。
原文下载：https://arxiv.org/pdf/1912.00759.pdf
