Non-intrusive Load Identification Based on Binary V-I Trajectory Color Coding and Residual Neural Network
In the non-intrusive load identification task, as the types of household appliances continue to increase, devices with small power differences but similar V-I trajectories can easily be misclassified. In response to these problems, this paper proposes a non-intrusive load identification method based on color coding and residual neural network. The collected high-frequency voltage and current data were preprocessed, and then the V-I trajectory was converted into a visual representation through binary trajectory mapping and HSV color coding. This not only incorporated rich electrical features into the V-I trajectory, but also enhanced the uniqueness of load characteristics. The method was finally verified using the PLAID public data set. The results show that the method proposed in this article significantly improves the recognition accuracy and can effectively distinguish individual electrical equipments.
non-invasive load identificationV-I trajectoryHSV color codingresidual neural network
万方数据
维普
