IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p2200-d773372.html
   My bibliography  Save this article

Low-Frequency Non-Intrusive Load Monitoring of Electric Vehicles in Houses with Solar Generation: Generalisability and Transferability

Author

Listed:
  • Apostolos Vavouris

    (Department of Electronic and Electrical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • Benjamin Garside

    (Department of Electronic and Electrical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • Lina Stankovic

    (Department of Electronic and Electrical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • Vladimir Stankovic

    (Department of Electronic and Electrical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

Abstract

Electrification of transportation is gaining traction as a viable alternative to vehicles that use fossil-fuelled internal combustion engines, which are responsible for a major part of carbon dioxide emissions. This global turn towards electrification of transportation is leading to an exponential energy and power demand, especially during late-afternoon and early-evening hours, that can lead to great challenges that electricity grids need to face. Therefore, accurate estimation of Electric Vehicle (EV) charging loads and time of use is of utmost importance for different participants in the electricity markets. In this paper, a scalable methodology for detecting, from smart meter data, household EV charging events and their load consumption with robust evaluation, is proposed. This is achieved via a classifier based on Random Decision Forests (RF) with load reconstruction via novel post-processing and a regression approach based on sequence-to-subsequence Deep Neural Network (DNN) with conditional Generative Adversarial Network (GAN). Emphasis is placed on the generalisability of the approaches over similar houses and cross-domain transferability to different geographical regions and different EV charging profiles, as this is a requirement of any real-case scenario. Lastly, the effectiveness of different performance and generalisation loss metrics is discussed. Both the RF classifier with load reconstruction and the DNN, based on the sequence-to-subsequence model, can accurately estimate the energy consumption of EV charging events in unseen houses at scale solely from household aggregate smart meter measurements at 1–15 min resolutions.

Suggested Citation

  • Apostolos Vavouris & Benjamin Garside & Lina Stankovic & Vladimir Stankovic, 2022. "Low-Frequency Non-Intrusive Load Monitoring of Electric Vehicles in Houses with Solar Generation: Generalisability and Transferability," Energies, MDPI, vol. 15(6), pages 1-27, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2200-:d:773372
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/2200/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/6/2200/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Bochao & Ye, Minxiang & Stankovic, Lina & Stankovic, Vladimir, 2020. "Non-intrusive load disaggregation solutions for very low-rate smart meter data," Applied Energy, Elsevier, vol. 268(C).
    2. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
    3. Thamer Alquthami & Abdullah Alsubaie & Mohannad Alkhraijah & Khalid Alqahtani & Saad Alshahrani & Murad Anwar, 2022. "Investigating the Impact of Electric Vehicles Demand on the Distribution Network," Energies, MDPI, vol. 15(3), pages 1-18, February.
    4. 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.
    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. 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).
    2. 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.
    3. Yan, Lei & Tian, Wei & Wang, Hong & Hao, Xing & Li, Zuyi, 2023. "Robust event detection for residential load disaggregation," Applied Energy, Elsevier, vol. 331(C).
    4. Elnaz Azizi & Mohammad T. H. Beheshti & Sadegh Bolouki, 2021. "Event Matching Classification Method for Non-Intrusive Load Monitoring," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    5. Bampos, Zafeirios N. & Laitsos, Vasilis M. & Afentoulis, Konstantinos D. & Vagropoulos, Stylianos I. & Biskas, Pantelis N., 2024. "Electric vehicles load forecasting for day-ahead market participation using machine and deep learning methods," Applied Energy, Elsevier, vol. 360(C).
    6. 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).
    7. 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.
    8. Pampa Sinha & Kaushik Paul & Sanchari Deb & Sulabh Sachan, 2023. "Comprehensive Review Based on the Impact of Integrating Electric Vehicle and Renewable Energy Sources to the Grid," Energies, MDPI, vol. 16(6), pages 1-39, March.
    9. Ma, Tai-Yu & Faye, Sébastien, 2022. "Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks," Energy, Elsevier, vol. 244(PB).
    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. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    12. Klemen Deželak & Klemen Sredenšek & Sebastijan Seme, 2023. "Energy Consumption and Grid Interaction Analysis of Electric Vehicles Based on Particle Swarm Optimisation Method," Energies, MDPI, vol. 16(14), pages 1-15, July.
    13. Çimen, Halil & Bazmohammadi, Najmeh & Lashab, Abderezak & Terriche, Yacine & Vasquez, Juan C. & Guerrero, Josep M., 2022. "An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring," Applied Energy, Elsevier, vol. 307(C).
    14. 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).
    15. Luan, Wenpeng & Tian, Longfei & Zhao, Bochao, 2023. "Leveraging hybrid probabilistic multi-objective evolutionary algorithm for dynamic tariff design," Applied Energy, Elsevier, vol. 342(C).
    16. Alexandra Märtz & Uwe Langenmayr & Sabrina Ried & Katrin Seddig & Patrick Jochem, 2022. "Charging Behavior of Electric Vehicles: Temporal Clustering Based on Real-World Data," Energies, MDPI, vol. 15(18), pages 1-26, September.
    17. 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).
    18. 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).
    19. Abdulaziz Almutairi & Naif Albagami & Sultanh Almesned & Omar Alrumayh & Hasmat Malik, 2023. "Electric Vehicle Load Estimation at Home and Workplace in Saudi Arabia for Grid Planners and Policy Makers," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    20. Hafsa Bousbiat & Yassine Himeur & Iraklis Varlamis & Faycal Bensaali & Abbes Amira, 2023. "Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond," Energies, MDPI, vol. 16(2), pages 1-22, January.

    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:15:y:2022:i:6:p:2200-:d:773372. 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.