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

Zhen Jiang, Di Shi, Xiaobin Guo, Guangyue Xu, Li Yu and Chaoyang Jing
Additional contact information
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

Energies, 2018, vol. 11, issue 6, 1-13

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.

Keywords: smart meter; robust analytics; imbalanced dataset; alternating least squares (ALS); DTW; cluster analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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