MSFEN-AM: A Non-intrusive Load Identification Method for Power Saving

In order to conserve power resources, researchers have been increasing their focus on load monitoring and identification technologies. However, current image classification methods for load identification face difficulties in extracting overall image contour features and complementing them based on image detail textures. To address this problem, we propose the MSFEN-AM network model, which consists of five parts: input part, downsampling module, bottlenick, upsampling module, and output part. We design a multi-scale feature dense extraction module in the network for feature reuse to improve information utilization. Additionally, we incorporated an attention mechanism to focus on extracting texture and edge information from the image. Furthermore, we introduced an attention gate mechanism to strengthen network selective features and suppress invalid information. Finally, we conducted experiments on the PLAID dataset and performed ablation tests on the multi-scale feature extraction modules and attention gating mechanism. The results show the superiority of our method.