An Approach of Household Power Appliance Monitoring Based on Machine Learning

Abstract

Monitoring household electrical consumption by employing appropriate techniques is of great significance to sustainable development of human society. This paper proposes one approach of nonintrusive appliance load monitoring (NIALM) for electrical consumption managing. This approach can automatically monitor the house power consumption of individual devices. It employs multiple-class support vector machine (M-SVM) to recognize different appliances. The approach is consisted of two stages. In stage one, harmonic feature analysis is applied on current signal. In stage two, a trained classifier based on M-SVM is applied to identify different appliances. This paper presents the principle of this approach, the experiment results on real data, and discussions on performance comparison with other study of supervised classification for household power appliance monitoring.