Non-intrusive load monitoring algorithm based on features of V–I trajectory

Abstract Non-intrusive load monitoring (NILM) can monitor the status of electrical appliances on-line and provide detailed power consumption data, which is the basis for customers to perform energy usage analyses and electricity management. The voltage–current (V–I) trajectory can be used as a load signature to represent the electrical characteristics of appliances with different statuses. Therefore, this paper proposes an NILM algorithm based on features of the V–I trajectory. The variation in the overall apparent power was used as the criterion of event detection, and the delta of the V–I trajectory was extracted by smoothing and interpolation. Then, ten V–I trajectory features were quantified based on physical significance, which accurately represented those appliances that had multiple built-in modes with distinct power consumption profiles. Finally, the support vector machine multi-classification algorithm was employed for load recognition. We tested the proposed algorithm on both the REDD database and laboratory data. The numerical results demonstrate that the algorithm has higher accuracy than the algorithm using other load features.