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.