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

TL;DR

This paper introduces a novel clustering algorithm using dynamic time warping to analyze smart water meter data, linking demand patterns to socio-economic characteristics, improving demand uncertainty modeling in water systems.

Contribution

The paper presents a new clustering method that outperforms existing techniques in identifying demand pattern groups and associating them with socio-economic factors.

Findings

Better cluster detection and pattern assignment than traditional methods

Identification of socio-economic traits within demand clusters

Potential to fill data gaps in hydraulic modeling

Abstract

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.