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Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning

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  • Mingzhi Yang

    (School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Yue Liu

    (School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Quanlong Liu

    (School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage. Nonintrusive load monitoring is a technique to separate the total electrical load of a single household into specific appliance loads. This problem is difficult because we aim to extract the energy consumption of each appliance by only using the total electrical load. Deep transfer learning is expected to solve this problem. This paper proposes a deep neural network model based on an attention mechanism. This model improves the traditional sequence-to-sequence model with a time-embedding layer and an attention layer so that it can be better applied in nonintrusive load monitoring. In particular, the improved model abandons the recurrent neural network structure and shortens the training time, which means it is more appropriate for use in model pretraining with large datasets. To verify the validity of the model, we selected three open datasets and compared them with the current leading model. The results show that transfer learning can effectively improve the prediction ability of the model, and the model proposed in this study has a better performance than the most advanced available model.

Suggested Citation

  • Mingzhi Yang & Yue Liu & Quanlong Liu, 2021. "Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning," Sustainability, MDPI, vol. 13(12), pages 1-11, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6546-:d:571144
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    References listed on IDEAS

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    1. İsmail Hakkı ÇAVDAR & Vahid FARYAD, 2019. "New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid," Energies, MDPI, vol. 12(7), pages 1-18, March.
    2. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
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    Cited by:

    1. S. M. Mezbahul Amin & Nazia Hossain & Molla Shahadat Hossain Lipu & Shabana Urooj & Asma Akter, 2023. "Development of a PV/Battery Micro-Grid for a Data Center in Bangladesh: Resilience and Sustainability Analysis," Sustainability, MDPI, vol. 15(22), pages 1-22, November.

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