Non-intrusive identification of building loads using EDCA-ShuffleNetV2 with fused feature visualization

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Abstract Non-intrusive load monitoring (NILM) enables monitoring of appliance operation status and energy consumption without additional meters, providing innovative data support for energy management. However, current NILM systems still face challenges such as insufficient load feature information and low load identification accuracy. To utilize the load feature information and improve the accuracy of load identification more effectively, this paper first selects loads with high energy consumption and high usage frequency as the research object, makes full use of the load feature information through feature fusion, and converts the fused typical load data into an image through the Gramian summation angular field, and further carries out the identification of loads that still have similarity after feature fusion. Secondly, this paper improves the ShuffleNetV2 model structure by embedding an efficient dual-channel attention (EDCA) module in the basic feature extraction module of ShuffleNetV2 to enhance it to extract adequate load feature information. A residual structure is also introduced to mitigate the information loss and gradient disappearance issues of the model. Finally, simulations are carried out in iAWE and AMPDS datasets, and the load identification accuracy of this method reaches 99.35%, while the model parameters and floating-point operations (FLOPs) are only 3.28 M and 1.5 G, respectively. In addition, the improved EDCA-ShuffleNetV2 model has obvious advantages in terms of comprehensive model performance compared with other models.