IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p12957-d1227001.html
   My bibliography  Save this article

A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images

Author

Listed:
  • Petros Papageorgiou

    (Department of Electrical & Computer Engineering, University of Western Macedonia, ZEP Campus, 50150 Kozani, Greece)

  • Dimitra Mylona

    (Department of Electrical & Computer Engineering, University of Western Macedonia, ZEP Campus, 50150 Kozani, Greece)

  • Konstantinos Stergiou

    (Department of Electrical & Computer Engineering, University of Western Macedonia, ZEP Campus, 50150 Kozani, Greece)

  • Aggelos S. Bouhouras

    (Department of Electrical & Computer Engineering, University of Western Macedonia, ZEP Campus, 50150 Kozani, Greece)

Abstract

Non-intrusive load monitoring (NILM) has been on the rise for more than three decades. Its main objective is non-intrusive load disaggregation into individual operating appliances. Recent studies have shown that a higher sampling rate in the aggregated measurements allows better performance regarding load disaggregation. In addition, recent developments in deep learning and, in particular, convolutional neural networks (CNNs) have facilitated load disaggregation using CNN models. Several methods have been described in the literature that combine both a higher sampling rate and a CNN-based NILM framework. However, these methods use only a small number of cycles of the aggregated signal, which complicates the practical application of real-time NILM. In this work, a high sampling rate time-driven CNN-based NILM framework is also proposed. However, a novel current harmonic distortion image extracted from 60 cycles of the aggregated signal is proposed, resulting in 1 s appliance classification with low computational complexity. Appliance classification performance is evaluated using the PLAID3 dataset for both single and combined appliance operation. In addition, a comparison is made with a method from the literature. The results highlight the robustness of the novel feature and confirm the real-time applicability of the proposed NILM framework.

Suggested Citation

  • Petros Papageorgiou & Dimitra Mylona & Konstantinos Stergiou & Aggelos S. Bouhouras, 2023. "A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12957-:d:1227001
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/12957/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/17/12957/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Miltiadis D. Lytras & Kwok Tai Chui, 2019. "The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications," Energies, MDPI, vol. 12(16), pages 1-7, August.
    2. Wesley Angelino de Souza & Fernando Deluno Garcia & Fernando Pinhabel Marafão & Luiz Carlos Pereira da Silva & Marcelo Godoy Simões, 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition," Energies, MDPI, vol. 12(14), pages 1-18, July.
    3. José Antonio Hoyo-Montaño & Guillermo Valencia-Palomo & Rafael Armando Galaz-Bustamante & Abel García-Barrientos & Daniel Fernando Espejel-Blanco, 2019. "Environmental Impacts of Energy Saving Actions in an Academic Building," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    4. Yan, Lei & Tian, Wei & Han, Jiayu & Li, Zuy, 2022. "Event-driven two-stage solution to non-intrusive load monitoring," Applied Energy, Elsevier, vol. 311(C).
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    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. Yan, Lei & Tian, Wei & Wang, Hong & Hao, Xing & Li, Zuyi, 2023. "Robust event detection for residential load disaggregation," Applied Energy, Elsevier, vol. 331(C).
    3. Xin Liang & Geoffrey Qiping Shen & Li Guo, 2019. "Optimizing Incentive Policy of Energy-Efficiency Retrofit in Public Buildings: A Principal-Agent Model," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    4. Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring," Energies, MDPI, vol. 15(9), pages 1-20, May.
    5. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    6. 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.
    7. 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.
    8. Gustavo Felipe Martin Nascimento & Frédéric Wurtz & Patrick Kuo-Peng & Benoit Delinchant & Nelson Jhoe Batistela, 2022. "Quantifying Compressed Air Leakage through Non-Intrusive Load Monitoring Techniques in the Context of Energy Audits," Energies, MDPI, vol. 15(9), pages 1-24, April.
    9. 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).
    10. Helena Bulińska-Stangrecka & Anna Bagieńska, 2021. "Culture-Based Green Workplace Practices as a Means of Conserving Energy and Other Natural Resources in the Manufacturing Sector," Energies, MDPI, vol. 14(19), pages 1-21, October.
    11. Davidson, Eleni & Schwartz, Yair & Williams, Joe & Mumovic, Dejan, 2024. "Resilience of the higher education sector to future climates: A systematic review of predicted building energy performance and modelling approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    12. Shin-Cheng Yeh & Ai-Wei Wu & Hui-Ching Yu & Homer C. Wu & Yi-Ping Kuo & Pei-Xuan Chen, 2021. "Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-34, August.
    13. Li, Chuyi & Zheng, Kedi & Guo, Hongye & Chen, Qixin, 2023. "A mixed-integer programming approach for industrial non-intrusive load monitoring," Applied Energy, Elsevier, vol. 330(PA).
    14. Nikolaos Virtsionis Gkalinikis & Christoforos Nalmpantis & Dimitris Vrakas, 2022. "Torch-NILM: An Effective Deep Learning Toolkit for Non-Intrusive Load Monitoring in Pytorch," Energies, MDPI, vol. 15(7), pages 1-20, April.
    15. Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    16. 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.
    17. Eder Andrade da Silva & Carlos Alejandro Urzagasti & Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Marco Roberto Cavallari & Oswaldo Hideo Ando Junior, 2022. "Development of a Self-Calibrated Embedded System for Energy Management in Low Voltage," Energies, MDPI, vol. 15(22), pages 1-21, November.
    18. Andreas Reinhardt & Lucas Pereira, 2021. "Special Issue: “Energy Data Analytics for Smart Meter Data”," Energies, MDPI, vol. 14(17), pages 1-3, August.
    19. Mohamed S. Abdalzaher & Mostafa M. Fouda & Mohamed I. Ibrahem, 2022. "Data Privacy Preservation and Security in Smart Metering Systems," Energies, MDPI, vol. 15(19), pages 1-19, October.
    20. Y., Nandakishora & Sahoo, Ranjit K. & S., Murugan & Gu, Sai, 2023. "4E analysis of the cryogenic CO2 separation process integrated with waste heat recovery," Energy, Elsevier, vol. 278(PA).

    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:jsusta:v:15:y:2023:i:17:p:12957-:d:1227001. 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.