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.

通过引入一种新颖的深度学习模型，该文章提出了一种改进的多标签非侵入式负载监测分类方法，以提高计算和能源效率，并且通过使用合成数据集进行测试，提出了一种比较不同模型的方法，从而更好地代表实际场景，相较于现有技术，该模型在减少碳足迹超过 23% 的同时，在以 REFIT 和 UK-DALE 数据集派生的数据上平均提供约 8 个百分点的性能改进。