New Appliance Detection for Nonintrusive Load Monitoring

Current methods for nonintrusive load monitoring (NILM) problems assume that the number of appliances in the target location is known, however, this may not be realistic. In real-world situations, the initial setup of the site can be known but new appliances may be added by users after a period of time, especially in a household or nonrestrictive scenarios. In this sense, current methods without detecting new appliances may not accurately monitor loads of different appliances and scenarios. In this paper, a novel new appliance detection method is proposed for NILM with imbalance classification for appliances switching ON or OFF. The prediction of appliances being switched ON or OFF is an important step in load monitoring and the switching on frequencies for coffee machine and air conditioning in a household are different, making the problem inherently imbalanced. Experimental results show that the proposed method yields outstanding performance against the well-known oversampling method, synthetic minority oversampling technique, on real NILM applications in scenarios with new appliances emerging.