Individual power profile estimation of residential appliances using low frequency smart meter data

We propose a new Non-Intrusive Load Monitoring (NILM) approach for appliances power profile/signal estimation at low sampling rate (1 s or greater). The proposed method relay on two main phases: identification of turned on appliance combination in a given time period and estimation of the active power consumption signal of each individual appliances in that combination. Unlike most existing NILM method that rely on multiple measurement at high sampling rates, appliances identification of this paper rely on a Karhunen Loéve (KL) expansion based spectral signature approach which only need active power measurements at a low sampling rate. Then the estimation of the power signal of the identified appliances is newly presented in this paper based on modified version of mean-shift clustering algorithm and Bayesian classification. The proposed method was validated by using two public databases: tracebase and REDD. The presented results demonstrate the ability of the proposed method to accurately estimate individual active power signal of turned on appliance combinations in real households.