Tangent Correlation Load Signature for Appliance Detection in NILM System

Non-intrusive load monitoring (NILM) can monitor the consumption of household appliances by analyzing the power input into the home. With the rapid increase in the number and types of household appliances, traditional NILM systems are gradually unable to cope with the challenges of appliance detection and recognition. In this work, we propose a novel load signature for NILM systems to realize new appliance detection as well as high-accuracy appliance recognition. Two tangent correlation methods, including tangent difference correlation and tangent-summation correlation, are in tegrated to construct the load signature. Experimental results on public NILM datasets confirm that the proposed method outperforms existing methods based on the voltage-current (V-I) trajectory, recurrence graph (RG), and Gramian angular field (GAF).