On measuring similarity in consumption patterns for smart meter time series

Time series data grouping is essential for empirical data analysis and data
summarization. Generally, grouping of time series data is based on similarity
measure (distance function), thus time series in the same group are similar. The
choice of similarity measure is very important during data grouping. In this paper
we investigate the issue of smart meter data grouping according to daily
consumption pattern using either Euclidian or correlation-based similarity. We find
that the correlation-based measure is superior for data grouping with respect to
consumption pattern.