Competitive Autoassociative Neural Networks for Electrical Appliance Identification for Non-Intrusive Load Monitoring

Abstract

This paper presents a new approach to electrical appliance identification for non-intrusive load monitoring (NILM). In the proposed method a set of autoassociative neural networks is trained so that each one is tuned with the characteristics of a particular electrical appliance. Then, the autoassociative neural networks are set up in a competitive parallel arrangement in which they compete with one another when a new input vector is entered and the closest recognition is accepted to identify the given electrical appliance. The system is trained to recognize specific types of electrical appliances and use the transient power signal obtained from the on/off events for each electrical appliance. To test the proposed method, three public datasets were used, they are, the reference energy disaggregation dataset (REDD), the United Kingdom recording domestic appliance-level electricity (UK-DALE) and the Tracebase dataset containing real residential measurements are used. The accuracy and F-score obtained for the three datasets show the applicability of the proposed method for NILM systems.