Adaptive non-intrusive Load Monitoring model using Bayesian learning

NILM is an electrical energy monitoring system that can be used in smart home/building. The system is equipped with sensors to measure the voltage and electric current large installed in the electrical panel. NILM methods are designed to measure the total power consumption signals at the entry point of the main electrical panel of a building, and then disaggregate it into the power consumption of individual appliances. This paper will take an approach relies on low frequency acquisition and steady state feature extraction and using Bayesian learning method for power disaggregation. In order to adapt to the change in the environment and to detect unknown state, this paper using an adaptive module that applied in the monitoring system.