  Non-intrusive load monitoring (NILM), aims to infer the power profiles of
appliances from the aggregated power signal via purely analytical methods.
Existing NILM methods are susceptible to various issues such as the noise and
transient spikes of the power signal, overshoots at the mode transition times,
close consumption values by different appliances, and unavailability of a large
training dataset. This paper proposes a novel event-based NILM classification
algorithm mitigating these issues. The proposed algorithm (i) filters power
consumption signals and accurately detects all events, (ii) extracts specific
features of appliances, such as operation modes and their respective power
consumption intervals, from their power consumption signals in the training
dataset, and (iii) labels with high accuracy each detected event of the
aggregated signal with an appliance mode transition. The algorithm is validated
using REDD with the results showing its effectiveness to accurately
disaggregate low frequency measured data by existing smart meters.
