Event-based Two-stage Non-intrusive Load Monitoring Method Involving Multi-dimensional Features

Abstract

This paper proposes an event-based two-stage Non-intrusive load monitoring (NILM) method involving multi-dimensional features, which is an essential technology for energy savings and management. First, capture appliance events using a goodness of fit test and then pair the on-off events. Then the multi-dimensional features are extracted to establish a feature library. In the first stage identification, several groups of events for the appliance have been divided, according to three features, including phase, steady active power and power peak. In the second stage identification, a 'one against the rest' support vector machine (SVM) model for each group is established to precisely identify the appliances. The proposed method is verified by using a public available dataset; the results show that the proposed method contains high generalization ability, less computation, and less training samples.