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A clustering approach to domestic electricity load profile characterisation using smart metering data

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  • McLoughlin, Fintan
  • Duffy, Aidan
  • Conlon, Michael

Abstract

The availability of increasing amounts of data to electricity utilities through the implementation of domestic smart metering campaigns has meant that traditional ways of analysing meter reading information such as descriptive statistics has become increasingly difficult. Key characteristic information to the data is often lost, particularly when averaging or aggregation processes are applied. Therefore, other methods of analysing data need to be used so that this information is not lost. One such method which lends itself to analysing large amounts of information is data mining. This allows for the data to be segmented before such aggregation processes are applied. Moreover, segmentation allows for dimension reduction thus enabling easier manipulation of the data.

Suggested Citation

  • McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    • Handle: RePEc:eee:appene:v:141:y:2015:i:c:p:190-199
    DOI: 10.1016/j.apenergy.2014.12.039
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    References listed on IDEAS

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