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<p>Most of existing non-invasive load monitoring (NILM) methods usually ignore the complementarity between temporal and spatial characteristics of appliance power data. To tackle this problem, this paper proposes a spatio-temporal attention fusion network with a sequence-to-point learning scheme for load disaggregation. Initially, a temporal feature extraction module is designed to extract temporal features over a large temporal receptive field. Then, an asymmetric inception module is designed for a multi-scale spatial feature extraction. The extracted temporal features and spatial features are concatenated, and fed into a polarized self-attention module to perform a spatio-temporal attention fusion, followed by two dense layers for final NILM predictions. Extensive experiments on two public datasets such as REDD and UK-DALE show the validity of the proposed method, outperforming the other used methods on NILM tasks.</p>

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Sequence-to-point learning based on spatio-temporal attention fusion network for non-intrusive load monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月17日

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  • Shiqing Zhang,
  • Youyao Fu,
  • Xiaoming Zhao,
  • Jiangxiong Fang,
  • Yadong Liu,
  • Xiaoli Wang,
  • Baochang Zhang,
  • Jun Yu

摘要

Most of existing non-invasive load monitoring (NILM) methods usually ignore the complementarity between temporal and spatial characteristics of appliance power data. To tackle this problem, this paper proposes a spatio-temporal attention fusion network with a sequence-to-point learning scheme for load disaggregation. Initially, a temporal feature extraction module is designed to extract temporal features over a large temporal receptive field. Then, an asymmetric inception module is designed for a multi-scale spatial feature extraction. The extracted temporal features and spatial features are concatenated, and fed into a polarized self-attention module to perform a spatio-temporal attention fusion, followed by two dense layers for final NILM predictions. Extensive experiments on two public datasets such as REDD and UK-DALE show the validity of the proposed method, outperforming the other used methods on NILM tasks.