  Non-Intrusive Load Monitoring (NILM) is the method of detecting an individual
device's energy signal from an aggregated energy consumption signature [1]. As
existing energy meters provide very little to no information regarding the
energy consumption of individual appliances apart from the aggregated power
rating, the spotting of individual appliances' energy usages by NILM will not
only provide consumers the feedback of appliance-specific energy usage but also
lead to the changes of their consumption behavior which facilitate energy
conservation. B Neenan et al. [2] have demonstrated that direct individual
appliance-specific energy usage signals lead to consumers' behavioral changes
which improves energy efficiency by as much as 15%. Upon disaggregation of an
energy signal, the signal needs to be classified according to the appropriate
appliance. Hence, the goal of this paper is to disaggregate total energy
consumption data to individual appliance signature and then classify
appliance-specific energy loads using a prominent supervised classification
method known as K-Nearest Neighbors (KNN). To perform this operation we have
used a publicly accessible dataset of power signals from several houses known
as the REDD dataset. Before applying KNN, data is preprocessed for each device.
Then KNN is applied to check whether their energy consumption signature is
separable or not. KNN is applied with K=5.
