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Dynamic time warping based non-intrusive load transient identification

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  • 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
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    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.
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    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).

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