IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p939-d1035587.html
   My bibliography  Save this article

An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature

Author

Listed:
  • Jiangang Lu

    (Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Ruifeng Zhao

    (Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Bo Liu

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Zhiwen Yu

    (Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Jinjiang Zhang

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Zhanqiang Xu

    (Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

Abstract

Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field’s development.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:939-:d:1035587
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/939/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/2/939/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    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.
    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. İsmail Hakkı Çavdar & Vahit Feryad, 2021. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid," Energies, MDPI, vol. 14(15), pages 1-21, July.
    2. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    3. Cristina Puente & Rafael Palacios & Yolanda González-Arechavala & Eugenio Francisco Sánchez-Úbeda, 2020. "Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques," Energies, MDPI, vol. 13(12), pages 1-20, June.
    4. 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.
    5. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    6. Andrea Mariscotti, 2022. "Non-Intrusive Load Monitoring Applied to AC Railways," Energies, MDPI, vol. 15(11), pages 1-27, June.
    7. Sarra Houidi & Dominique Fourer & François Auger & Houda Ben Attia Sethom & Laurence Miègeville, 2021. "Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning," Energies, MDPI, vol. 14(9), pages 1-28, May.
    8. 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.
    9. Debnath, Ramit & Bardhan, Ronita & Misra, Ashwin & Hong, Tianzhen & Rozite, Vida & Ramage, Michael H., 2022. "Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models," Energy Policy, Elsevier, vol. 164(C).
    10. Everton Luiz de Aguiar & André Eugenio Lazzaretti & Bruna Machado Mulinari & Daniel Rodrigues Pipa, 2021. "Scattering Transform for Classification in Non-Intrusive Load Monitoring," Energies, MDPI, vol. 14(20), pages 1-20, October.
    11. Christos Athanasiadis & Dimitrios Doukas & Theofilos Papadopoulos & Antonios Chrysopoulos, 2021. "A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption," Energies, MDPI, vol. 14(3), pages 1-23, February.
    12. Yichao Xie & Bowen Zhou & Zhenyu Wang & Bo Yang & Liaoyi Ning & Yanhui Zhang, 2023. "Industrial Carbon Footprint (ICF) Calculation Approach Based on Bayesian Cross-Validation Improved Cyclic Stacking," Sustainability, MDPI, vol. 15(19), pages 1-35, September.
    13. Yang, Chao & Liang, Gaoqi & Liu, Jinjie & Liu, Guolong & Yang, Hongming & Zhao, Junhua & Dong, Zhaoyang, 2023. "A non-intrusive carbon emission accounting method for industrial corporations from the perspective of modern power systems," Applied Energy, Elsevier, vol. 350(C).
    14. Douglas Paulo Bertrand Renaux & Fabiana Pottker & Hellen Cristina Ancelmo & André Eugenio Lazzaretti & Carlos Raiumundo Erig Lima & Robson Ribeiro Linhares & Elder Oroski & Lucas da Silva Nolasco & Lu, 2020. "A Dataset for Non-Intrusive Load Monitoring: Design and Implementation," Energies, MDPI, vol. 13(20), pages 1-35, October.
    15. Inoussa Laouali & Isaías Gomes & Maria da Graça Ruano & Saad Dosse Bennani & Hakim El Fadili & Antonio Ruano, 2022. "Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks," Energies, MDPI, vol. 15(23), pages 1-29, November.
    16. Xi He & Heng Dong & Wanli Yang & Jun Hong, 2022. "A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring," Energies, MDPI, vol. 15(6), pages 1-15, March.
    17. Georgios Yiasoumas & Lazar Berbakov & Valentina Janev & Alessandro Asmundo & Eneko Olabarrieta & Andrea Vinci & Giovanni Baglietto & George E. Georghiou, 2023. "Key Aspects and Challenges in the Implementation of Energy Communities," Energies, MDPI, vol. 16(12), pages 1-24, June.
    18. Xiao-Yu Zhang & Stefanie Kuenzel & José-Rodrigo Córdoba-Pachón & Chris Watkins, 2020. "Privacy-Functionality Trade-Off: A Privacy-Preserving Multi-Channel Smart Metering System," Energies, MDPI, vol. 13(12), pages 1-30, June.
    19. Hari Prasad Devarapalli & Venkata Samba Sesha Siva Sarma Dhanikonda & Sitarama Brahmam Gunturi, 2021. "Demand-Side Management for Improvement of the Power Quality in Smart Homes Using Non-Intrusive Identification of Appliance Usage Patterns with the True Power Factor," Energies, MDPI, vol. 14(16), pages 1-19, August.
    20. Netzah Calamaro & Moshe Donko & Doron Shmilovitz, 2021. "A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements," Energies, MDPI, vol. 14(21), pages 1-37, November.

    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:gam:jeners:v:16:y:2023:i:2:p:939-:d:1035587. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.