  The problem of identifying end-use electrical appliances from their
individual consumption profiles, known as the appliance identification problem,
is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated
plug-wise metering. Therefore, appliance identification has received dedicated
studies with various electric appliance signatures, classification models, and
evaluation datasets. In this paper, we propose a neural network ensembles
approach to address this problem using high resolution measurements. The models
are trained on the raw current and voltage waveforms, and thus, eliminating the
need for well engineered appliance signatures. We evaluate the proposed model
on a publicly available appliance dataset from 55 residential buildings, 11
appliance categories, and over 1000 measurements. We further study the
stability of the trained models with respect to training dataset, sampling
frequency, and variations in the steady-state operation of appliances.
