Comparing time-series clustering approaches for individual electrical load patterns

==Abstract==

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

==Full Document==

<pdf>Media:Draft_Samper_168233917_3779_08315868.pdf</pdf>