IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0296979.html
   My bibliography  Save this article

Non-invasive load monitoring based on deep learning to identify unknown loads

Author

Listed:
  • Anshun Su
  • Zehua Du
  • Bo Yin

Abstract

With the rapid development of smart grids, society has become increasingly urgent to solve the problems of low energy utilization efficiency and high energy consumption. In this context, load identification has become a key element in formulating scientific and effective energy consumption plans and reducing unnecessary energy waste. However, traditional load identification methods mainly focus on known electrical equipment, and accurate identification of unknown electrical equipment still faces significant challenges. A new encoding feature space based on Triplet neural networks is proposed in this paper to detect unknown electrical appliances using convex hull coincidence degree. Additionally, transfer learning is introduced for the rapid updating of the pre-classification model’s self-incrementing class with the unknown load. In experiments, the effectiveness of our method is successfully tested on the PLAID dataset. The accuracy of unknown load identification reached 99.23%. Through this research, we expect to bring a new idea to the field of load identification to meet the urgent need for the identification of unknown electrical appliances in the development of smart grids.

Suggested Citation

  • Anshun Su & Zehua Du & Bo Yin, 2024. "Non-invasive load monitoring based on deep learning to identify unknown loads," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-20, February.
    • Handle: RePEc:plo:pone00:0296979
    DOI: 10.1371/journal.pone.0296979
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296979
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0296979&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0296979?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Anwar Ul Haq & Hans-Arno Jacobsen, 2018. "Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review," Energies, MDPI, vol. 11(1), pages 1-22, January.
    2. Richard H. R. Hahnloser & Rahul Sarpeshkar & Misha A. Mahowald & Rodney J. Douglas & H. Sebastian Seung, 2000. "Correction: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit," Nature, Nature, vol. 408(6815), pages 1012-1012, December.
    3. Richard H. R. Hahnloser & Rahul Sarpeshkar & Misha A. Mahowald & Rodney J. Douglas & H. Sebastian Seung, 2000. "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit," Nature, Nature, vol. 405(6789), pages 947-951, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmed A Metwally & Philip S Yu & Derek Reiman & Yang Dai & Patricia W Finn & David L Perkins, 2019. "Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-16, February.
    2. Kim, Seongsu & Kim, Junghwan, 2023. "Assessing fuel economy and NOx emissions of a hydrogen engine bus using neural network algorithms for urban mass transit systems," Energy, Elsevier, vol. 275(C).
    3. Zhang, Wen & Yan, Shaoshan & Li, Jian & Tian, Xin & Yoshida, Taketoshi, 2022. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    4. Maoz Shamir, 2009. "The Temporal Winner-Take-All Readout," PLOS Computational Biology, Public Library of Science, vol. 5(2), pages 1-13, February.
    5. Oostwal, Elisa & Straat, Michiel & Biehl, Michael, 2021. "Hidden unit specialization in layered neural networks: ReLU vs. sigmoidal activation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    6. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    7. Joanne C Wen & Cecilia S Lee & Pearse A Keane & Sa Xiao & Ariel S Rokem & Philip P Chen & Yue Wu & Aaron Y Lee, 2019. "Forecasting future Humphrey Visual Fields using deep learning," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-14, April.
    8. Kei Nakagawa & Masaya Abe & Junpei Komiyama, 2019. "A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy," Papers 1910.01491, arXiv.org.
    9. Jingfen Lan & Ziheng Liao & A. K. Alvi Haque & Qiang Yu & Kun Xie & Yang Guo, 2024. "CNVbd: A Method for Copy Number Variation Detection and Boundary Search," Mathematics, MDPI, vol. 12(3), pages 1-15, January.
    10. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    11. Baiti-Ahmad Awaluddin & Chun-Tang Chao & Juing-Shian Chiou, 2023. "Investigating Effective Geometric Transformation for Image Augmentation to Improve Static Hand Gestures with a Pre-Trained Convolutional Neural Network," Mathematics, MDPI, vol. 11(23), pages 1-23, November.
    12. González-Muñiz, Ana & Díaz, Ignacio & Cuadrado, Abel A. & García-Pérez, Diego, 2022. "Health indicator for machine condition monitoring built in the latent space of a deep autoencoder," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    13. Ke, Shaowei & Zhao, Chen, 2024. "From local utility to neural networks," Journal of Mathematical Economics, Elsevier, vol. 113(C).
    14. Hancock, Thomas O. & Broekaert, Jan & Hess, Stephane & Choudhury, Charisma F., 2020. "Quantum probability: A new method for modelling travel behaviour," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 165-198.
    15. Bernhard Nessler & Michael Pfeiffer & Lars Buesing & Wolfgang Maass, 2013. "Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-30, April.
    16. Artem Kuriksha, 2021. "An Economy of Neural Networks: Learning from Heterogeneous Experiences," Papers 2110.11582, arXiv.org.
    17. Bodendorf, Frank & Xie, Qiao & Merkl, Philipp & Franke, Jörg, 2022. "A multi-perspective approach to support collaborative cost management in supplier-buyer dyads," International Journal of Production Economics, Elsevier, vol. 245(C).
    18. Li Zhang & Qun Hao & Jie Cao, 2023. "Attention-Based Fine-Grained Lightweight Architecture for Fuji Apple Maturity Classification in an Open-World Orchard Environment," Agriculture, MDPI, vol. 13(2), pages 1-20, January.
    19. Gianluca De Nard & Simon Hediger & Markus Leippold, 2022. "Subsampled factor models for asset pricing: The rise of Vasa," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1217-1247, September.
    20. repec:plo:pcbi00:1004039 is not listed on IDEAS
    21. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0296979. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.