An efficient scalable time-frequency method for trackingudenergy usage of domestic appliances using a two-stepudclassification algorithmud

摘要

Load identification is the practice of measuring electrical signals in a domestic environment in order to identify which electrical appliances are consuming power. One reason for developing a load identification system is to reduce power consumption by increasing consumers’ awareness of which appliances consume most energy. The thesis outlines the development of a load disaggregation method that measures the aggregate electrical signals of a domestic environment and extracts features to identify each power consuming appliance. A single sensor is deployed at the main incoming power point, to sample the aggregate current signal. The method senses when an appliance switches ON or OFF and uses a two-step classification algorithm to identify which appliance has caused the event. Parameters from the current in the temporal and frequency domains are used as features to de- fine each appliance. These parameters are the steady state current harmonics and the rate of change of the transient signal. Each appliance’s electrical characteristics are distinguishable using these parameters. There are three types of loads that an appliance can fall into, linear nonreactive, linear reactive or nonlinear reactive. It has been found that by identifying the load type first, and then using a second classifier to identify individual appliances within these types, the overall accuracy of the identification algorithm is improved.