IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v335y2025ics0360544225037429.html

Real-time NILM: A lightweight and low power approach

Author

Listed:
  • Dong, Ze
  • Zhang, Xiaohu
  • Cai, Shiyi
  • Yang, Yuhang
  • Jiang, Wei
  • Song, Yaqi
  • Zhao, Wenqing
  • Zhang, Dongyang

Abstract

Non-intrusive load monitoring (NILM) is a great significance for energy conservation, cost reduction, and environmental protection. It helps with better energy management and planning by accurately identifying the energy consumption of each load. However, existing research approaches have high complexity and poor real-time performance, making it difficult to be applied in edge devices. Therefore, this paper proposes a real-time lightweight NILM approach that can be applied to edge devices. This approach is composed of the following main steps: (i) We propose a Time&Voltage Dual-Reference-Based Time-Domain Window Subtraction (TVDR-TDWS) Method to separate the transient waveform when a single load is activated from the aggregated transient current data. (ii) A lightweight model with high accuracy and few parameters is designed for load monitoring. The experimental results show that the proposed model can accurately identify the load from the aggregated current data by training with the transient current waveform of a single load activation. The accuracy rates on the TDHA dataset and the PLAID dataset reach 94.69% and 81.92% respectively. (iii) Finally we deployed the lightweight model on a microcontroller with the Cortex-M7 architecture(STM32H7 series), and it has a significant model inference time down to 9.067 ms. This makes a new approach to NILM in practical field applications.

Suggested Citation

  • Dong, Ze & Zhang, Xiaohu & Cai, Shiyi & Yang, Yuhang & Jiang, Wei & Song, Yaqi & Zhao, Wenqing & Zhang, Dongyang, 2025. "Real-time NILM: A lightweight and low power approach," Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225037429
    DOI: 10.1016/j.energy.2025.138100
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225037429
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.138100?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
    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.
    3. Hasan Rafiq & Xiaohan Shi & Hengxu Zhang & Huimin Li & Manesh Kumar Ochani, 2020. "A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing," Energies, MDPI, vol. 13(9), pages 1-26, May.
    4. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    5. Lei Yao & Jinhao Wang & Chen Zhao, 2024. "Non-Intrusive Load Monitoring Based on Multiscale Attention Mechanisms," Energies, MDPI, vol. 17(8), pages 1-23, April.
    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. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    2. Pusceddu, Gabriella & Manca, Simone & Massidda, Luca, 2025. "Fine-tuning non-intrusive load monitoring model through user interaction: A practical approach to appliance recognition with limited labeled data," Applied Energy, Elsevier, vol. 391(C).
    3. Todic, Tamara & Stankovic, Vladimir & Stankovic, Lina, 2023. "An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem," Applied Energy, Elsevier, vol. 341(C).
    4. Liu, Yu & Liu, Wei & Shen, Yiwen & Zhao, Xin & Gao, Shan, 2021. "Toward smart energy user: Real time non-intrusive load monitoring with simultaneous switching operations," Applied Energy, Elsevier, vol. 287(C).
    5. 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.
    6. Hari Prasad Devarapalli & V. S. S. Siva Sarma Dhanikonda & Sitarama Brahmam Gunturi, 2020. "Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion," Energies, MDPI, vol. 13(18), pages 1-15, September.
    7. Tekler, Zeynep Duygu & Low, Raymond & Zhou, Yuren & Yuen, Chau & Blessing, Lucienne & Spanos, Costas, 2020. "Near-real-time plug load identification using low-frequency power data in office spaces: Experiments and applications," Applied Energy, Elsevier, vol. 275(C).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Abayomi A. Adebiyi & Mathew Habyarimana, 2025. "Systematic Review of Optimization Methodologies for Smart Home Energy Management Systems," Energies, MDPI, vol. 18(19), pages 1-28, October.
    13. Xia, Yingqi & Sun, Gengchen & Wang, Yanfeng & Yang, Qing & Wang, Qingrui & Ba, Shusong, 2024. "A novel carbon emission estimation method based on electricity‑carbon nexus and non-intrusive load monitoring," Applied Energy, Elsevier, vol. 360(C).
    14. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    15. Ce Peng & Guoying Lin & Shaopeng Zhai & Yi Ding & Guangyu He, 2020. "Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model," Energies, MDPI, vol. 13(21), pages 1-19, October.
    16. Jiateng Song & Hongbin Wang & Mingxing Du & Lei Peng & Shuai Zhang & Guizhi Xu, 2021. "Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network," Energies, MDPI, vol. 14(3), pages 1-15, January.
    17. Papageorgiou, Petros G. & Christoforidis, Georgios C. & Bouhouras, Aggelos S., 2025. "NILM in high frequency domain: A critical review on recent trends and practical challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 213(C).
    18. 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).
    19. Andrea Mariscotti, 2022. "Non-Intrusive Load Monitoring Applied to AC Railways," Energies, MDPI, vol. 15(11), pages 1-27, June.
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    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:eee:energy:v:335:y:2025:i:c:s0360544225037429. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.