  Non-intrusive load monitoring (NILM) is a key cost-effective technology for
monitoring power consumption and contributing to several challenges encountered
when transiting to an efficient, sustainable, and competitive energy efficiency
environment. This paper proposes a smart NILM system based on a novel local
power histogramming (LPH) descriptor, in which appliance power signals are
transformed into 2D space and short histograms are extracted to represent each
device. Specifically, short local histograms are drawn to represent individual
appliance consumption signatures and robustly extract appliance-level data from
the aggregated power signal. Furthermore, an improved k-nearest neighbors
(IKNN) algorithm is presented to reduce the learning computation time and
improve the classification performance. This results in highly improving the
discrimination ability between appliances belonging to distinct categories. A
deep evaluation of the proposed LPH-IKNN based solution is investigated under
different data sets, in which the proposed scheme leads to promising
performance. An accuracy of up to 99.65% and 98.51% has been achieved on GREEND
and UK-DALE data sets, respectively. While an accuracy of more than 96% has
been attained on both WHITED and PLAID data sets. This proves the validity of
using 2D descriptors to accurately identify appliances and create new
perspectives for the NILM problem.
