IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v11y2019i2p51-d207067.html
   My bibliography  Save this article

A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data

Author

Listed:
  • Quanbo Yuan

    (School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang 065000, China
    The School of Computer Software, Tianjin University, Tianjin 300354, China)

  • Huijuan Wang

    (School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang 065000, China
    State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China)

  • Botao Wu

    (Department of Automotive Engineering, Hebei Institute of Machinery and Electricity, Xintai 054000, China)

  • Yaodong Song

    (School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang 065000, China)

  • Hejia Wang

    (The School of Computer Software, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments.

Suggested Citation

  • Quanbo Yuan & Huijuan Wang & Botao Wu & Yaodong Song & Hejia Wang, 2019. "A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data," Future Internet, MDPI, vol. 11(2), pages 1-13, February.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:2:p:51-:d:207067
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/11/2/51/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/11/2/51/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    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. Brudermueller, Tobias & Kreft, Markus & Fleisch, Elgar & Staake, Thorsten, 2023. "Large-scale monitoring of residential heat pump cycling using smart meter data," Applied Energy, Elsevier, vol. 350(C).

    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. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    2. Chatzigeorgiou, I.M. & Andreou, G.T., 2021. "A systematic review on feedback research for residential energy behavior change through mobile and web interfaces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    3. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    4. Zhou, Yang & Shi, Zhixiong & Shi, Zhengyu & Gao, Qing & Wu, Libo, 2019. "Disaggregating power consumption of commercial buildings based on the finite mixture model," Applied Energy, Elsevier, vol. 243(C), pages 35-46.
    5. Lang, Corey & Okwelum, Edson, 2015. "The mitigating effect of strategic behavior on the net benefits of a direct load control program," Energy Economics, Elsevier, vol. 49(C), pages 141-148.
    6. Coelho, Igor M. & Coelho, Vitor N. & Luz, Eduardo J. da S. & Ochi, Luiz S. & Guimarães, Frederico G. & Rios, Eyder, 2017. "A GPU deep learning metaheuristic based model for time series forecasting," Applied Energy, Elsevier, vol. 201(C), pages 412-418.
    7. Iana Vassileva & Javier Campillo, 2016. "Consumers’ Perspective on Full-Scale Adoption of Smart Meters: A Case Study in Västerås, Sweden," Resources, MDPI, vol. 5(1), pages 1-18, January.
    8. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    9. Benjamin Völker & Marc Pfeifer & Philipp M. Scholl & Bernd Becker, 2020. "A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring," Energies, MDPI, vol. 14(1), pages 1-26, December.
    10. Astier, Nicolas, 2018. "Comparative feedbacks under incomplete information," Resource and Energy Economics, Elsevier, vol. 54(C), pages 90-108.
    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. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    13. Liu, Bo & Luan, Wenpeng & Yu, Yixin, 2017. "Dynamic time warping based non-intrusive load transient identification," Applied Energy, Elsevier, vol. 195(C), pages 634-645.
    14. Nicolas Astier, 2016. "Comparative Feedbacks under Incomplete Information," Working Papers hal-01465189, HAL.
    15. Mohamed Aymane Ahajjam & Daniel Bonilla Licea & Chaimaa Essayeh & Mounir Ghogho & Abdellatif Kobbane, 2020. "MORED: A Moroccan Buildings’ Electricity Consumption Dataset," Energies, MDPI, vol. 13(24), pages 1-22, December.
    16. 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.
    17. Changho Shin & Seungeun Rho & Hyoseop Lee & Wonjong Rhee, 2019. "Data Requirements for Applying Machine Learning to Energy Disaggregation," Energies, MDPI, vol. 12(9), pages 1-19, May.
    18. Matteo Caldera & Asad Hussain & Sabrina Romano & Valerio Re, 2023. "Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform," Energies, MDPI, vol. 16(2), pages 1-23, January.
    19. Hosseini, Sayed Saeed & Agbossou, Kodjo & Kelouwani, Sousso & Cardenas, Alben, 2017. "Non-intrusive load monitoring through home energy management systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1266-1274.
    20. Yulia V. Vertakova & Vladimir A. Plotnikov, 2019. "The Integrated Approach to Sustainable Development: The Case of Energy Efficiency and Solid Waste Management," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 194-201.

    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:jftint:v:11:y:2019:i:2:p:51-:d:207067. 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.