Dynamic Time Warping Clustering to Discover Socioeconomic Characteristics in Smart Water Meter Data

D. B. Steffelbauer, M. Blokker, S. G. Buchberger, Arno Knobbe, E. Abraham

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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Socioeconomic characteristics are influencing the temporal and spatial variability of water demand, which are the biggest source of uncertainties within water distribution system modeling. Improving current knowledge of these influences can be utilized to decrease demand uncertainties. This paper aims to link smart water meter data to socioeconomic user characteristics by applying a novel clustering algorithm that uses a dynamic time warping metric on daily demand patterns. The approach is tested on simulated and measured single-family home data sets. It is shown that the novel algorithm performs better compared with 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 socioeconomic characteristics (e.g., employment status and 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.
Original languageEnglish
Article number04021026
Number of pages12
JournalJournal of Water Resources Planning and Management
Issue number6
Publication statusPublished - 2021

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Smart meters
  • clustering
  • machine learning (ML)
  • Demand modeling
  • Dynamic time warping
  • Socioeconomic characteristics


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