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Socio-economic characteristics are influencing the temporal and spatialvariability of water demand - the biggest source of uncertainties within waterdistribution system modeling. Improving our knowledge on these influences canbe utilized to decrease demand uncertainties. This paper aims to link smartwater meter data to socio-economic user characteristics by applying a novelclustering 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 usedclustering methods, both, in finding the right number of clusters as well asassigning patterns correctly. Additionally, the methodology can be used toidentify outliers within clusters of demand patterns. Furthermore, this studyinvestigates 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, theproposed methods in combination with stochastic demand models can be used tofill data-gaps in hydraulic models.

Dynamic Time Warping Clustering to Discover Socio-Economic Characteristics in Smart Water Meter Data