Comparing clustering approaches for smart meter time series: Investigating the influence of dataset properties on performance

Luke W. Yerbury, Ricardo J.G.B. Campello, G.C. Livingston , Mark Goldsworthy and O’Neil, Lachlan

Applied Energy, 2025, vol. 391, issue C, No S0306261925005410

Abstract: The widespread adoption of smart meters for monitoring energy consumption has generated vast quantities of high-resolution time series data which remain underutilised. While clustering has emerged as a fundamental tool for mining smart meter time series (SMTS) data, selecting appropriate clustering methods remains challenging despite numerous comparative studies. These studies often rely on problematic methodologies and consider a limited scope of methods, frequently overlooking compelling methods from the broader time series clustering literature. Consequently, they struggle to provide dependable guidance for practitioners designing their own clustering approaches.

Keywords: Smart meters; Smart grids; Load pattern; Load profile; Time series; Clustering; Comparative study (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125811

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