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Nonintrusive load monitoring in residential households with low-resolution data

Author

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  • Shi, Xin
  • Ming, Hao
  • Shakkottai, Srinivas
  • Xie, Le
  • Yao, Jianguo

Abstract

Detailed information on individual appliance consumption is beneficial for improving energy efficiency and managing demand response. Nonintrusive load monitoring (NILM) aims to estimate the device-level energy consumption from the load data of an entire household. Because the majority of households can only provide load data at a normal smart-meter level, this paper introduces a novel similar time window (STW) algorithm to perform NILM with lower-resolution data. Derived from k-nearest neighbors (kNN), the proposed STW algorithm compares both the time and frequency domain similarities between windows of interest and historical data segments, and then selects the most similar time windows by instance-based learning to determine the device-level energy consumption. The desirable features of this algorithm include (1) reductions in the costs of and requirements for sensing equipment, (2) improvements in privacy preservation, and (3) a significant enhancement of the computational efficiency. To facilitate the selection of the data resolution and to satisfy the NILM application requirements in a cost-effective way, the paper also investigates the relationship among the input/output data resolution, time window length and prediction accuracy. To enable the generalizability of this algorithm, a cross-prediction approach is proposed to obtain the device-level consumption from a “library” of a group of households, without knowing each one’s own historical data. Simulation results using four real-world public datasets demonstrate the competitive performance of the proposed STW algorithm with respect to traditionally used approaches for low-resolution NILM.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:252:y:2019:i:c:66
    DOI: 10.1016/j.apenergy.2019.05.086
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    Cited by:

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    3. Li, Dandan & Li, Jiangfeng & Zeng, Xin & Stankovic, Vladimir & Stankovic, Lina & Xiao, Changjiang & Shi, Qingjiang, 2023. "Transfer learning for multi-objective non-intrusive load monitoring in smart building," Applied Energy, Elsevier, vol. 329(C).
    4. Li, Chuyi & Zheng, Kedi & Guo, Hongye & Chen, Qixin, 2023. "A mixed-integer programming approach for industrial non-intrusive load monitoring," Applied Energy, Elsevier, vol. 330(PA).
    5. 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).
    6. Hari Prasad Devarapalli & V. S. S. Siva Sarma Dhanikonda & Sitarama Brahmam Gunturi, 2020. "Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion," Energies, MDPI, vol. 13(18), pages 1-15, September.
    7. Todic, Tamara & Stankovic, Vladimir & Stankovic, Lina, 2023. "An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem," Applied Energy, Elsevier, vol. 341(C).
    8. Xie, Xiangmin & Chen, Daolian, 2022. "Data-driven dynamic harmonic model for modern household appliances," Applied Energy, Elsevier, vol. 312(C).

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