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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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    Cited by:

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    12. 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.
    13. 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.
    14. 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).
    15. 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).
    16. 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.
    17. 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.
    18. 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.
    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).

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