Efficient Discovery of Recurrent Routine Behaviours in Smart Meter Time Series by Growing Subsequences

Wang, Jin; Cardell-Oliver, Rachel; Liu, Wei

doi:10.1007/978-3-319-18032-8_41

Cited by 3 publications

(1 citation statement)

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“…The patterns were qualitatively interpreted as residential, commercial, office, industrial, or noise. Cardell-Oliver et al (2016) identified groups of similar households by features of their high-magnitude water-use behaviors based on previous work (Cardell-Oliver 2013a, b;Wang et al 2015). Cominola et al (2018) applied customer segmentation analysis simultaneously on water and electricity data by clustering extracted eigenbehaviors and linked the clusters to a list of user psychographic features.…”

Section: Related Workmentioning

confidence: 99%

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

Steffelbauer

Blokker

Buchberger

et al. 2021

J. Water Resour. Plann. Manage.

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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.

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Section: Related Workmentioning

confidence: 99%