Title: More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring Show Chinese title

Title: 电费背后的更多内容：一种非侵入式负载监测的双DNN方法

Abstract: Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances. In this way, considerable energy savings could be achieved by enhancing household's awareness of energy usage. Recent investigations have shown that deep neural networks (DNNs) based approaches are promising for the NILM task. Nevertheless, they normally ignore the inherent properties of appliance operations in the network design, potentially leading to implausible results. We are thus motivated to develop the dual Deep Neural Networks (dual-DNN), which aims to i) take advantage of DNNs' learning capability of latent features and ii) empower the DNN architecture with identification ability of universal properties. Specifically in the design of dual-DNN, we adopt one subnetwork to measure power ratings of different appliances' operation states, and the other subnetwork to identify the running states of target appliances. The final result is then obtained by multiplying these two network outputs and meanwhile considering the multi-state property of household appliances. To enforce the sparsity property in appliance's state operating, we employ median filtering and hard gating mechanisms to the subnetwork for state identification. Compared with the state-of-the-art NILM methods, our dual-DNN approach demonstrates a 21.67% performance improvement in average on two public benchmark datasets. Show Chinese abstract

Abstract: 非侵入性负载监测（NILM）是一个广为人知的单通道盲源分离问题，旨在将家庭能源消耗分解为各个电器的分项能源使用情况。 通过提高家庭对能源使用的意识，可以实现显著的节能效果。 最近的研究表明，基于深度神经网络（DNN）的方法在NILM任务中具有前景。 然而，它们通常在网络设计中忽略了电器操作的固有特性，可能导致不合理的结果。 因此，我们旨在开发双深度神经网络（双-DNN），其目标是i）利用DNN对潜在特征的学习能力，ii）赋予DNN架构识别通用特性的能力。 具体来说，在双-DNN的设计中，我们采用一个子网络来测量不同电器操作状态的功率等级，另一个子网络用于识别目标电器的运行状态。 最终结果是通过乘以这两个网络的输出并同时考虑家用电器的多状态特性得到的。 为了在电器状态运行中强制稀疏性特性，我们将中值滤波和硬门控机制应用于状态识别的子网络。 与最先进的NILM方法相比，我们的双-DNN方法在两个公开基准数据集上的平均性能提高了21.67%。