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Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping

Author

Listed:
  • Zhen Jiang

    (Southern China Power Grid EPRI, 11 Kexiang Rd., Guangzhou 510663, China)

  • Di Shi

    (eMIT, LLC., 125 N Lake Ave., Pasadena, CA 91101, USA)

  • Xiaobin Guo

    (Southern China Power Grid EPRI, 11 Kexiang Rd., Guangzhou 510663, China)

  • Guangyue Xu

    (eMIT, LLC., 125 N Lake Ave., Pasadena, CA 91101, USA)

  • Li Yu

    (Southern China Power Grid EPRI, 11 Kexiang Rd., Guangzhou 510663, China)

  • Chaoyang Jing

    (eMIT, LLC., 125 N Lake Ave., Pasadena, CA 91101, USA)

Abstract

This paper presents a robust data-driven framework for clustering large-scale daily chronological load curves from smart meters, with a focus on the challenges encountered in practice. The first challenge is the low data quality issue due to bad and missing data, which has been a major obstacle for various in-depth analyses of smart meter data. A novel Smoothed Alternating Least Squares (SALS) approach is proposed to recover missing/bad smart meter data by taking advantage of their low-rank property. The second challenge is brought by different data reporting rates of smart meters. A Dynamic Time Warping (DTW)-based approach is proposed that is more efficient and eliminates the need for data interpolation or measurement downsampling. The proposed approach enables flexible data collection strategies and gateway locations to meet various smart grid performance requirements. The proposed framework is tested through experiments using real-world smart meter data.

Suggested Citation

  • Zhen Jiang & Di Shi & Xiaobin Guo & Guangyue Xu & Li Yu & Chaoyang Jing, 2018. "Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping," Energies, MDPI, vol. 11(6), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1401-:d:149775
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    Citations

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    Cited by:

    1. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.
    2. Antonio Moretti & Charalampos Pitas & George Christofi & Emmanuel Bué & Modesto Gabrieli Francescato, 2020. "Grid Integration as a Strategy of Med-TSO in the Mediterranean Area in the Framework of Climate Change and Energy Transition," Energies, MDPI, vol. 13(20), pages 1-22, October.

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