Non-Intrusive Load Identification Method Based on Gramian Angular Difference Field Image Coding

Non-intrusive load monitoring,as an essential means for fine-grained management of household electricity consumption,plays a significant role in promoting energy conservation and emission reduction for achieving the dual-carbon goal.However,it is challenging to achieve high-precision load identification using a single voltage-current trajectory image.Therefore,a non-intrusive load identification method based on the fusion of Gramian angular difference field(GADF)image coding is proposed.First,the high-frequency steady-state data collected by the device are preprocessed to obtain a complete base-wave period current and voltage signal.Then,the one-dimensional voltage and current signals are encoded separately using the GADF to generate the corresponding two-dimensional feature images,and load identification is performed via superimposed fusion input to a neural network based on a convolutional block attention module.The public datasets PLAID and WHITED are used for testing experiments to verify the effectiveness of the proposed method.The results indicate that the method has a high recognition accuracy,with average accuracies of 99.45%and 99.24%for the PLAID and WHITED datasets,respectively.