  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.
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 an evaluation
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 energy consumption reduced
by more than 23% while providing on average approximately 8 percentage points
in performance improvement when evaluating on data derived from REFIT and
UK-DALE datasets. We also show a 12 percentage point performance advantage of
the proposed DL based model over a random forest model and observe performance
degradation with the increase of the number of devices in the household, namely
with each additional 5 devices, the average performance degrades by
approximately 7 percentage points.
