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Transfer learning for multi-objective non-intrusive load monitoring in smart building

Author

Listed:
  • Li, Dandan
  • Li, Jiangfeng
  • Zeng, Xin
  • Stankovic, Vladimir
  • Stankovic, Lina
  • Xiao, Changjiang
  • Shi, Qingjiang

Abstract

Buildings represent 39% of global greenhouse gas emissions, thus reducing carbon emissions in buildings is of importance to greenhouse gas emissions reductions. This requires understanding how electricity is utilized in the buildings, then optimizing electricity management to seek conservation of energy. Non-intrusive load monitoring (NILM) is a technique that disaggregates a house’s total load to estimate each appliance’s electric power usage. Several strategies for estimating one appliance at a time (one-to-one model) have been presented and experimentally proven to be effective, with two mainstream trends: appliance transfer learning and cross-domain transfer learning. The former refers to the transfer between different types of appliances in the same data domain, while the latter refers to the transfer between different data domains for the same type of appliance. Different from the previous work, this paper explores the approach of adopting one model for all appliances (one-to-many model) and proposes a novel transfer learning scheme, that incorporates appliance transfer learning and cross-domain transfer learning. Thus, a well-trained model can be transferred and utilized to effectively estimate the power consumption in another data set for all appliances, which demands fewer measurements and only one model. Three public data sets, REFIT, REDD, and UK-DALE, are used in our experiments. Further, a set of smart electricity meters was deployed in a practical non-residential building to validate the proposed method. The results demonstrate the accuracy and practicality compared to start-of-the-art one-to-one NILM transferred models.

Suggested Citation

  • Li, Dandan & Li, Jiangfeng & Zeng, Xin & Stankovic, Vladimir & Stankovic, Lina & Xiao, Changjiang & Shi, Qingjiang, 2023. "Transfer learning for multi-objective non-intrusive load monitoring in smart building," Applied Energy, Elsevier, vol. 329(C).
  • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922014805
    DOI: 10.1016/j.apenergy.2022.120223
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    References listed on IDEAS

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    1. Shi, Xin & Ming, Hao & Shakkottai, Srinivas & Xie, Le & Yao, Jianguo, 2019. "Nonintrusive load monitoring in residential households with low-resolution data," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
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    3. Rashid, Haroon & Singh, Pushpendra & Stankovic, Vladimir & Stankovic, Lina, 2019. "Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?," Applied Energy, Elsevier, vol. 238(C), pages 796-805.
    4. Stankovic, L. & Stankovic, V. & Liao, J. & Wilson, C., 2016. "Measuring the energy intensity of domestic activities from smart meter data," Applied Energy, Elsevier, vol. 183(C), pages 1565-1580.
    5. Liu, Chao & Akintayo, Adedotun & Jiang, Zhanhong & Henze, Gregor P. & Sarkar, Soumik, 2018. "Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network," Applied Energy, Elsevier, vol. 211(C), pages 1106-1122.
    6. Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.
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    Cited by:

    1. Wang, Zhongrui & Xu, Yonghai & He, Sheng & Yuan, Jindou & Yang, Heng & Pan, Mingming, 2023. "A non-intrusive method of industrial load disaggregation based on load operating states and improved grey wolf algorithm," Applied Energy, Elsevier, vol. 351(C).
    2. Jiangang Lu & Ruifeng Zhao & Bo Liu & Zhiwen Yu & Jinjiang Zhang & Zhanqiang Xu, 2023. "An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature," Energies, MDPI, vol. 16(2), pages 1-15, January.
    3. Jiachuan Shi & Dingrui Zhi & Rao Fu, 2023. "Research on a Non-Intrusive Load Recognition Algorithm Based on High-Frequency Signal Decomposition with Improved VI Trajectory and Background Color Coding," Mathematics, MDPI, vol. 12(1), pages 1-20, December.

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