Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Non-intrusive load monitoring (NILM) is a technique for analyzing changes in the voltage and current flowing through the main feeder and determining the appliances in operation as well as their energy consumption. With the increase in amount and type of electric loads nowadays, it is of increasing significance to extract unique load signatures and build robust classification models for NILM. However, the electric loads of different households differ materially from one another, which makes it difficult to collect enough label data and train classification models with strong representation and generalization ability. In this paper, a voltage–current (<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>) trajectory enabled transfer learning method has been proposed for NILM. Different from the existing methods, a deep learning model pretrained on a visual recognition dataset is transferred to train the classifier for NILM, linking the knowledge between different domains. Moreover, the <inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> trajectory is also transferred to visual representation by color encoding, which not only enhances the load signature’s uniqueness but also enables the NILM implementation of transfer learning. The experimental results on NILM datasets show that the proposed method significantly improves the accuracy and can be efficiently generalized compared with state-of-the-art methods.