  Socio-economic characteristics are influencing the temporal and spatial
variability of water demand - the biggest source of uncertainties within water
distribution system modeling. Improving our knowledge on these influences can
be utilized to decrease demand uncertainties. This paper aims to link smart
water meter data to socio-economic user characteristics by applying a novel
clustering algorithm that uses dynamic time warping on daily demand patterns.
The approach is tested on simulated and measured single family home datasets.
We show that the novel algorithm performs better compared to commonly used
clustering methods, both, in finding the right number of clusters as well as
assigning patterns correctly. Additionally, the methodology can be used to
identify outliers within clusters of demand patterns. Furthermore, this study
investigates which socio-economic characteristics (e.g. employment status,
number of residents) are prevalent within single clusters and, consequently,
can be linked to the shape of the cluster's barycenters. In future, the
proposed methods in combination with stochastic demand models can be used to
fill data-gaps in hydraulic models.

Machine Learning;Systems and Control;Signal Processing;Systems and Control