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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.

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