Non-intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing

Abstract

With the development of smart grids, appliance-level data information plays a vital role in smart power consumption. Nowadays, appliance signatures detected by non-intrusive load monitoring (NILM) can be used for anomaly detection, demand response, and electricity management. These applications increase the requirements for the accuracy of appliance identification in NILM. And it has been proved that the voltage–current (V–I) trajectory can be applied as an effective load signature to represent the electrical characteristics of appliances with different statuses in previous researches. In this paper, a V–I trajectory enabled asymmetric deep supervised hashing (ADSH) method has been proposed for NILM. ADSH method converts load identification into the large-scale approximate nearest neighbour search. Different from the existing methods, ADSH treats the query images and database images in an asymmetric way in order to improve accuracy. More specifically, ADSH learns a deep hash function only for query images, while the hash codes for database images are directly learned. The experimental results on REDD and PLAID datasets show that the proposed method significantly improves the accuracy of load identification compared with state-of-art methods.