This work positions the task of grouping electricity load time series among the vast field of clustering, and highlights corresponding research issues. A selection of the most performant time-series clustering approaches from the signal processing community are compared on the same dataset, composed by domestic electricity load profiles from Spain. The cross-correlation-based distance of Paparrizos and Gravano (2015) is shown to provide the best trade-off between clustering accuracy and CPU times

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Published on 01/01/2017

DOI: 10.1049/opa-cired.2017.1222
Licence: CC BY-NC-SA license

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