IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v195y2017icp634-645.html
   My bibliography  Save this article

Dynamic time warping based non-intrusive load transient identification

Author

Listed:
  • Liu, Bo
  • Luan, Wenpeng
  • Yu, Yixin

Abstract

Non-intrusive load monitoring (NILM) is a novel and cost-effective technology for monitoring load electricity energy consumption details. In the event-based NILM, transient power waveform (TPW) time-series can be used as signatures to identify the transients of the electrical appliances in the aggregated load, and then to determine their operating states, estimate their power demand and cumulative energy consumption. In this paper, for load transient identification, the dynamic time warping (DTW) algorithm is adopted for the first time to measure the similarity between the variable-length raw TPW sample and template time-series. Accordingly, a nearest neighbor transient identification method is proposed to identify the appliance creating the TPW sample time-series, in which the DTW-based integrated distance is used to measure the similarity of TPW signatures. Three schemes to calculate the integrated distance are designed, combining multiple types of TPW signatures. Comparison tests with existing methods are conducted using public datasets. The comparison test results indicate that the proposed load transient identification method cannot only improve the accuracy of load transient identification, but also is easy to implement at a reasonable cost. Ultimately, the proposed method is implemented in an embedded system. The field test results show that it can identify the operating states of electrical appliances with high accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:195:y:2017:i:c:p:634-645
    DOI: 10.1016/j.apenergy.2017.03.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917302209
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.03.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Kobus, Charlotte B.A. & Klaassen, Elke A.M. & Mugge, Ruth & Schoormans, Jan P.L., 2015. "A real-life assessment on the effect of smart appliances for shifting households’ electricity demand," Applied Energy, Elsevier, vol. 147(C), pages 335-343.
    2. 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.
    3. Belaïd, Fateh & Garcia, Thomas, 2016. "Understanding the spectrum of residential energy-saving behaviours: French evidence using disaggregated data," Energy Economics, Elsevier, vol. 57(C), pages 204-214.
    4. Chang, Hsueh-Hsien & Yang, Hong-Tzer, 2009. "Applying a non-intrusive energy-management system to economic dispatch for a cogeneration system and power utility," Applied Energy, Elsevier, vol. 86(11), pages 2335-2343, November.
    5. 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.
    6. 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.
    7. 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.
    8. Hsueh-Hsien Chang, 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses," Energies, MDPI, vol. 5(11), pages 1-21, November.
    9. Fateh Belaid & Thomas Garcia, 2016. "Understanding the spectrum of residential energy-saving behaviours: French evidence using disaggregated data," Post-Print halshs-01321365, HAL.
    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. Younghoon Kwak & Jihyun Hwang & Taewon Lee, 2018. "Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building," Energies, MDPI, vol. 11(4), pages 1-22, April.
    2. 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.
    3. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    4. Huijuan Wang & Wenrong Yang & Tingyu Chen & Qingxin Yang, 2019. "An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data," Sustainability, MDPI, vol. 11(1), pages 1-16, January.
    5. Luan, Wenpeng & Wei, Zun & Liu, Bo & Yu, Yixin, 2022. "Non-intrusive power waveform modeling and identification of air conditioning load," Applied Energy, Elsevier, vol. 324(C).
    6. Song, Chunhe & Jing, Wei & Zeng, Peng & Yu, Haibin & Rosenberg, Catherine, 2018. "Energy consumption analysis of residential swimming pools for peak load shaving," Applied Energy, Elsevier, vol. 220(C), pages 176-191.
    7. 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).
    8. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    9. Liu, Bo & Hou, Yufan & Luan, Wenpeng & Liu, Zishuai & Chen, Sheng & Yu, Yixin, 2023. "A divide-and-conquer method for compression and reconstruction of smart meter data," Applied Energy, Elsevier, vol. 336(C).
    10. Bode, Gerrit & Schreiber, Thomas & Baranski, Marc & Müller, Dirk, 2019. "A time series clustering approach for Building Automation and Control Systems," Applied Energy, Elsevier, vol. 238(C), pages 1337-1345.
    11. Pascal A. Schirmer & Iosif Mporas & Akbar Sheikh-Akbari, 2021. "Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation," Energies, MDPI, vol. 14(9), pages 1-16, April.
    12. Augustyn Wójcik & Piotr Bilski & Robert Łukaszewski & Krzysztof Dowalla & Ryszard Kowalik, 2021. "Identification of the State of Electrical Appliances with the Use of a Pulse Signal Generator," Energies, MDPI, vol. 14(3), pages 1-26, January.
    13. Shi, Xin & Ming, Hao & Shakkottai, Srinivas & Xie, Le & Yao, Jianguo, 2019. "Nonintrusive load monitoring in residential households with low-resolution data," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    14. Tomasz Jasiński, 2020. "Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)," Energies, MDPI, vol. 13(5), pages 1-16, March.
    15. 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).
    16. 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.
    17. Huang, Jing & Qin, Rui, 2024. "Elman neural network considering dynamic time delay estimation for short-term forecasting of offshore wind power," Applied Energy, Elsevier, vol. 358(C).
    18. Liu, Yu & Liu, Congxiao & Ling, Qicheng & Zhao, Xin & Gao, Shan & Huang, Xueliang, 2021. "Toward smart distributed renewable generation via multi-uncertainty featured non-intrusive interactive energy monitoring," Applied Energy, Elsevier, vol. 303(C).
    19. 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).
    20. Wang, Shuangyuan & Li, Ran & Evans, Adrian & Li, Furong, 2020. "Regional nonintrusive load monitoring for low voltage substations and distributed energy resources," Applied Energy, Elsevier, vol. 260(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. Bonfigli, Roberto & Principi, Emanuele & Fagiani, Marco & Severini, Marco & Squartini, Stefano & Piazza, Francesco, 2017. "Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models," Applied Energy, Elsevier, vol. 208(C), pages 1590-1607.
    2. Quaglione, Davide & Cassetta, Ernesto & Crociata, Alessandro & Sarra, Alessandro, 2017. "Exploring additional determinants of energy-saving behaviour: The influence of individuals' participation in cultural activities," Energy Policy, Elsevier, vol. 108(C), pages 503-511.
    3. 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.
    4. Xinkuo Xu & Liyan Han, 2017. "Diverse Effects of Consumer Credit on Household Carbon Emissions at Quantiles: Evidence from Urban China," Sustainability, MDPI, vol. 9(9), pages 1-25, September.
    5. 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.
    6. Rafael de Arce & Ramón Mahía, 2019. "Drivers of Electricity Poverty in Spanish Dwellings: A Quantile Regression Approach," Energies, MDPI, vol. 12(11), pages 1-18, May.
    7. Salomé Bakaloglou and Dorothée Charlier, 2019. "Energy Consumption in the French Residential Sector: How Much do Individual Preferences Matter?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    8. Yan Liu & Rong Liu & Xin Jiang, 2019. "What drives low-carbon consumption behavior of Chinese college students? The regulation of situational factors," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 173-191, January.
    9. Hasan Rafiq & Xiaohan Shi & Hengxu Zhang & Huimin Li & Manesh Kumar Ochani, 2020. "A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing," Energies, MDPI, vol. 13(9), pages 1-26, May.
    10. Song, Qingbin & Li, Jinhui & Duan, Huabo & Yu, Danfeng & Wang, Zhishi, 2017. "Towards to sustainable energy-efficient city: A case study of Macau," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 504-514.
    11. 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.
    12. Belaïd, Fateh & Roubaud, David & Galariotis, Emilios, 2019. "Features of residential energy consumption: Evidence from France using an innovative multilevel modelling approach," Energy Policy, Elsevier, vol. 125(C), pages 277-285.
    13. Lévy, Jean-Pierre & Belaïd, Fateh, 2018. "The determinants of domestic energy consumption in France: Energy modes, habitat, households and life cycles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2104-2114.
    14. Shigeru Matsumoto & Hajime Sugeta, 2019. "Efficiency investment and curtailment action: complements or substitutes," Working Papers e137, Tokyo Center for Economic Research.
    15. Belaïd, Fateh & Bakaloglou, Salomé & Roubaud, David, 2018. "Direct rebound effect of residential gas demand: Empirical evidence from France," Energy Policy, Elsevier, vol. 115(C), pages 23-31.
    16. Anwar Ul Haq & Hans-Arno Jacobsen, 2018. "Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review," Energies, MDPI, vol. 11(1), pages 1-22, January.
    17. Du, Feng & Yue, Hong & Zhang, Jiangfeng, 2023. "Influence of advertisement control to residential energy savings in large networks," Applied Energy, Elsevier, vol. 333(C).
    18. Kearns, Ade & Whitley, Elise & Curl, Angela, 2019. "Occupant behaviour as a fourth driver of fuel poverty (aka warmth & energy deprivation)," Energy Policy, Elsevier, vol. 129(C), pages 1143-1155.
    19. Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
    20. Belaïd, Fateh, 2017. "Untangling the complexity of the direct and indirect determinants of the residential energy consumption in France: Quantitative analysis using a structural equation modeling approach," Energy Policy, Elsevier, vol. 110(C), pages 246-256.

    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:195:y:2017:i:c:p:634-645. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.