IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i20p7207-d1265505.html
   My bibliography  Save this article

Applying the Geometric Features of Cumulative Sums to the Development of Event Detection

Author

Listed:
  • Men-Shen Tsai

    (Graduate Institute of Automation Technology, Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Yen-Kuang Lin

    (College of Mechanical & Electrical Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

Abstract

As a result of the severe energy shortage and the greenhouse effect, experts worldwide have been devoted to solving energy management problems. Smart grid construction is an essential technology for mastering energy allocation. Smart grids enable end users to adjust their energy consumption via incentive measures, reduce the frequency of power supply instability, and improve energy efficiency. Non-intrusive load monitoring (NILM) is a vital technology for smart grid construction. One of the fundamental steps of NILM is event detection. Proper event detection can increase the accuracy of load identification. Among traditional methods, especially the event detection method developed with the CUSUM method, although the accuracy is reasonable, the precision, recall, and f 1 score are not relatively better. Thus, there is an opportunity to improve the performance of CUSUM. Additionally, many studies focus on the step-like event, but the long-transient event is often overlooked in event detection. Therefore, in this study, it was observed that when the transient current deviates from the steady-state current, the transient current can be regarded as a key indicator for event detection. With this observation, a method is proposed to convert the root mean square (RMS) current into a cumulative sum (CUSUM) diagram method and identify turning points representing events from the CUSUM geometry. Once the slope of the turning point has been determined, event detection is achieved. Compared with traditional methods, the proposed method is easy to implement, its recognition rate can reach around 98%, and the window length is reduced from 5 s to 3 s.

Suggested Citation

  • Men-Shen Tsai & Yen-Kuang Lin, 2023. "Applying the Geometric Features of Cumulative Sums to the Development of Event Detection," Energies, MDPI, vol. 16(20), pages 1-25, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7207-:d:1265505
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/20/7207/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/20/7207/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amitay Kligman & Arbel Yaniv & Yuval Beck, 2023. "Energy Disaggregation of Type I and II Loads by Means of Birch Clustering and Watchdog Timers," Energies, MDPI, vol. 16(7), pages 1-21, March.
    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.
    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. 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.
    2. Jeewon Park & Oladayo S. Ajani & Rammohan Mallipeddi, 2023. "Optimization-Based Energy Disaggregation: A Constrained Multi-Objective Approach," Mathematics, MDPI, vol. 11(3), pages 1-13, January.
    3. 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.
    4. Ngoc-Son Truong & Duc Long Luong & Quang Trung Nguyen, 2023. "BIM to BEM Transition for Optimizing Envelope Design Selection to Enhance Building Energy Efficiency and Cost-Effectiveness," Energies, MDPI, vol. 16(10), pages 1-24, May.
    5. Abubakar, I. & Khalid, S.N. & Mustafa, M.W. & Shareef, Hussain & Mustapha, M., 2017. "Application of load monitoring in appliances’ energy management – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 235-245.
    6. Wu, Xin & Jiao, Dian & Liang, Kaixin & Han, Xiao, 2019. "A fast online load identification algorithm based on V-I characteristics of high-frequency data under user operational constraints," Energy, Elsevier, vol. 188(C).
    7. Aydinalp Koksal, Merih & Rowlands, Ian H. & Parker, Paul, 2015. "Energy, cost, and emission end-use profiles of homes: An Ontario (Canada) case study," Applied Energy, Elsevier, vol. 142(C), pages 303-316.
    8. Chao Min & Guoquan Wen & Zhaozhong Yang & Xiaogang Li & Binrui Li, 2019. "Non-Intrusive Load Monitoring System Based on Convolution Neural Network and Adaptive Linear Programming Boosting," Energies, MDPI, vol. 12(15), pages 1-23, July.
    9. Kwok Tai Chui & Miltiadis D. Lytras & Anna Visvizi, 2018. "Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption," Energies, MDPI, vol. 11(11), pages 1-20, October.
    10. 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).
    11. Zhao, Pengxiang & Dong, Zhao Yang & Meng, Ke & Kong, Weicong & Yang, Jiajia, 2021. "Household power usage pattern filtering-based residential electricity plan recommender system," Applied Energy, Elsevier, vol. 298(C).
    12. Song, Chunhe & Jing, Wei & Zeng, Peng & Rosenberg, Catherine, 2017. "An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management," Applied Energy, Elsevier, vol. 195(C), pages 1-12.
    13. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    14. 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.
    15. Dinesh, Chinthaka & Welikala, Shirantha & Liyanage, Yasitha & Ekanayake, Mervyn Parakrama B. & Godaliyadda, Roshan Indika & Ekanayake, Janaka, 2017. "Non-intrusive load monitoring under residential solar power influx," Applied Energy, Elsevier, vol. 205(C), pages 1068-1080.
    16. Hsueh-Hsien Chang & Nguyen Viet Linh, 2017. "Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems," Energies, MDPI, vol. 10(5), pages 1-20, April.
    17. 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.
    18. Oladayo S. Ajani & Abhishek Kumar & Rammohan Mallipeddi & Swagatam Das & Ponnuthurai Nagaratnam Suganthan, 2022. "Benchmarking Optimization-Based Energy Disaggregation Algorithms," Energies, MDPI, vol. 15(5), pages 1-19, February.
    19. Rashid, Haroon & Singh, Pushpendra & Stankovic, Vladimir & Stankovic, Lina, 2019. "Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?," Applied Energy, Elsevier, vol. 238(C), pages 796-805.

    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:16:y:2023:i:20:p:7207-:d:1265505. 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.