IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v247y2019icp643-656.html
   My bibliography  Save this article

Data compression approach for the home energy management system

Author

Listed:
  • Jia, Kunqi
  • Guo, Ge
  • Xiao, Jucheng
  • Zhou, Huan
  • Wang, Zhihua
  • He, Guangyu

Abstract

As a typical energy-cyber-physical system (e-CPS), home energy management system (HEMS) plays a critical role in power systems by accommodating higher levels of renewable generation, reducing power costs, and decreasing consumer energy bills. HEMS can help understand the home appliances energy use and learn the users’ preference so as to optimize home appliances operation and achieve higher energy efficiency. HEMS needs massive historical and real-time data for the above applications. Since HEMS is always based on a wireless sensor network, a more effective online data compression approach is necessary. The efficient data compression methods can not only relieve data transmission pressure and reduce data storage overhead, but also enhance data analysis efficiency. This paper proposes an online pattern-based data compression approach for the data generated by home appliances. The proposed approach first discovers the patterns of the time series data and then utilizes these patterns for the online data compression. The pattern discovery method in the proposed approach includes an online adaptive segmenting algorithm with incremental processing technique and a similarity metric based on piecewise statistic distance. The key issues of parameter selection and data reconstruction are also presented. Real-world common home appliance datasets are employed for comparing the performance of the proposed approach with those of six state-of-the-art algorithms. The experimental results demonstrate the outperformance of the proposed approach. Further complexity analysis shows that the proposed approach has linear time complexity. To the best of our knowledge, this is the first paper that performs online data compression based on the extracted patterns of the time series.

Suggested Citation

  • Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
  • Handle: RePEc:eee:appene:v:247:y:2019:i:c:p:643-656
    DOI: 10.1016/j.apenergy.2019.04.078
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191930741X
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    2. Tsai, Men-Shen & Lin, Yu-Hsiu, 2012. "Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation," Applied Energy, Elsevier, vol. 96(C), pages 55-73.
    3. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    4. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    5. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    6. Hajibandeh, Neda & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators," Applied Energy, Elsevier, vol. 212(C), pages 721-732.
    7. Markovič, Rene & Gosak, Marko & Grubelnik, Vladimir & Marhl, Marko & Virtič, Peter, 2019. "Data-driven classification of residential energy consumption patterns by means of functional connectivity networks," Applied Energy, Elsevier, vol. 242(C), pages 506-515.
    8. Jin, Xin & Baker, Kyri & Christensen, Dane & Isley, Steven, 2017. "Foresee: A user-centric home energy management system for energy efficiency and demand response," Applied Energy, Elsevier, vol. 205(C), pages 1583-1595.
    9. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    10. Ahmadi-Karvigh, Simin & Ghahramani, Ali & Becerik-Gerber, Burcin & Soibelman, Lucio, 2018. "Real-time activity recognition for energy efficiency in buildings," Applied Energy, Elsevier, vol. 211(C), pages 146-160.
    11. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    12. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Li, Lanlan, 2018. "Compression of smart meter big data: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 59-69.
    13. Keshtkar, Azim & Arzanpour, Siamak, 2017. "An adaptive fuzzy logic system for residential energy management in smart grid environments," Applied Energy, Elsevier, vol. 186(P1), pages 68-81.
    14. Murray, D.M. & Liao, J. & Stankovic, L. & Stankovic, V., 2016. "Understanding usage patterns of electric kettle and energy saving potential," Applied Energy, Elsevier, vol. 171(C), pages 231-242.
    15. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
    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. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).

    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:appene:v:247:y:2019:i:c:p:643-656. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.