  Non-intrusive load monitoring (NILM) as the process of extracting the usage
pattern of appliances from the aggregated power signal is among successful
approaches aiding residential energy management. In recent years, high volume
datasets on power profiles have become available, which has helped make
classification methods employed for the NILM purpose more effective and more
accurate. However, the presence of multi-mode appliances and appliances with
close power values have remained influential in worsening the computational
complexity and diminishing the accuracy of these algorithms. To tackle these
challenges, we propose an event-based classification process, in the first
phase of which the $K$-nearest neighbors method, as a fast classification
technique, is employed to extract power signals of appliances with exclusive
non-overlapping power values. Then, two deep learning models, which consider
the water consumption of some appliances as a novel signature in the network,
are utilized to distinguish between appliances with overlapping power values.
In addition to power disaggregation, the proposed process as well extracts the
water consumption profiles of specific appliances. To illustrate the proposed
process and validate its efficiency, seven appliances of the AMPds are
considered, with the numerical classification results showing marked
improvement with respect to the existing classification-based NILM techniques.
