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

Author

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

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.

Suggested Citation

  • Yerbury, Luke W. & Campello, Ricardo J.G.B. & Livingston Jr, G.C. & Goldsworthy, Mark & O’Neil, Lachlan, 2025. "Comparing clustering approaches for smart meter time series: Investigating the influence of dataset properties on performance," Applied Energy, Elsevier, vol. 391(C).
    • Handle: RePEc:eee:appene:v:391:y:2025:i:c:s0306261925005410
    DOI: 10.1016/j.apenergy.2025.125811
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