Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand response applications and energy management systems as well as for awareness raising and motivation for improvements in energy efficiency and reduction in the carbon footprint. Recently, classical machine learning and deep learning (DL) techniques became very popular and proved as highly effective for NILM classification, but with the growing complexity these methods are faced with significant computational and energy demands during both their training and operation. In this paper, we introduce a novel DL model aimed at enhanced multi-label classification of NILM with improved computation and energy efficiency. We also propose a testing methodology for comparison of different models using data synthesized from the measurement datasets so as to better represent real-world scenarios. Compared to the state-of-the-art, the proposed model has its carbon footprint reduced by more than 23% while providing on average approximately 8 percentage points in performance improvement when testing on data derived from REFIT and UK-DALE datasets.

翻译：非侵入式负荷监测（NILM）是指通过单个计量点获取设备级数据，从而测量家庭或企业的总用电量的过程。设备级数据可直接用于需求响应应用和能源管理系统，同时有助于提高节能意识、促进能效改进并减少碳足迹。近年来，经典机器学习与深度学习（DL）技术在NILM分类中变得非常流行并被证明高度有效，但随着方法复杂性的增加，这些技术在训练和运行过程中面临着巨大的计算和能源需求。本文提出了一种新颖的深度学习模型，旨在通过改进计算和能源效率，提升NILM的多标签分类性能。我们还提出了一种基于测量数据集合成数据的测试方法，用于不同模型的比较，以更好地模拟真实场景。与现有最先进方法相比，所提出的模型在基于REFIT和UK-DALE数据集测试时，碳足迹减少超过23%，同时平均性能提升约8个百分点。