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Non-intrusive load identification can improve the interaction efficiency between the power supply side and the user side of the grid. Applying this technology can alleviate the problem of energy shortage and is a key technique for achieving efficient management on the user side. In response to the cumbersome process of manually selecting load features and the low accuracy of identification in traditional machine learning algorithms for non-intrusive load identification, this paper proposes a method that transforms the one-dimensional reactive electric signal of the load into a two-dimensional image using Gram coding and utilizes the Residual Attention Network (RAN) for load classification and recognition. By transforming the one-dimensional electrical signal into a two-dimensional image as the input to the RAN network, this approach retains the original load information while providing richer information for the RAN network to extract load features. Furthermore, the RAN network effectively addresses the poor performance and gradient vanishing issues of deep learning networks through bottleneck residual blocks. Finally, experiments were conducted on a public dataset to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Non-intrusive load identification can improve the interaction efficiency between the power supply side and the user side of the grid. Applying this technology can alleviate the problem of energy shortage and is a key technique for achieving efficient management on the user side. In response to the cumbersome process of manually selecting load features and the low accuracy of identification in traditional machine learning algorithms for non-intrusive load identification, this paper proposes a method that transforms the one-dimensional reactive electric signal of the load into a two-dimensional image using Gram coding and utilizes the Residual Attention Network (RAN) for load classification and recognition. By transforming the one-dimensional electrical signal into a two-dimensional image as the input to the RAN network, this approach retains the original load information while providing richer information for the RAN network to extract load features. Furthermore, the RAN network effectively addresses the poor performance and gradient vanishing issues of deep learning networks through bottleneck residual blocks. Finally, experiments were conducted on a public dataset to verify the effectiveness of the proposed method.