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Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks

Author

Listed:
  • Mao Wang

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 1851, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China)

  • Dandan Liu

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 1851, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China)

  • Changzhi Li

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 1851, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China)

Abstract

At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within the context of generalization performance, failing to meet the decomposition accuracy requirements when applied to novel scenarios. To address this issue, a non-intrusive load decomposition method based on instance-batch normalization network is proposed. This method uses an encoder-decoder structure with attention mechanism, in which skip connections are introduced at the corresponding layers of the encoder and decoder. In this way, the decoder can reconstruct a more accurate power sequence of the target. The proposed model was tested on two public datasets, REDD and UKDALE, and the performance was compared with mainstream algorithms. The results show that the F 1 score was higher by an average of 18.4 when compared with mainstream algorithms. Additionally, the mean absolute error reduced by an average of 25%, and the root mean square error was reduced by an average of 22%.

Suggested Citation

  • Mao Wang & Dandan Liu & Changzhi Li, 2023. "Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks," Energies, MDPI, vol. 16(7), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:2940-:d:1105151
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    References listed on IDEAS

    as
    1. Veronica Piccialli & Antonio M. Sudoso, 2021. "Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network," Energies, MDPI, vol. 14(4), pages 1-16, February.
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