An empirical water consumer segmentation and the characterisation of consumption patterns underpinning demand peaks

Segmentation of Social Media Platforms in Terms of Perceived Benefits: Cluster Analysis on Brand Followers

“…In addition to these studies, there are many studies conducted in recent years regarding customer segmentation (Chen et al, 2019;Funk et al, 2021;Abu-Bakar et al, 2021;Moon et al, 2021;Hedhilia et al, 2021;Špička & Zdeňka, 2022;Budhathoki et al, 2022;Wang et al, 2022;Caracciolo et al, 2022;Vigneau et al, 2022;Carla Kuesten et al, 2022). It has been observed that these studies on customer segmentation are mostly about organic products, food, and supplements.…”

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

“…A higher number of solutions are based on discovering patterns in water consumption data trough clustering [112][113][114]. Dziminska et al [112] apply two clustering methods, hierarchical agglomeration and K-means, to build histograms of diurnal water consumption for apartment buildings.…”

“…They discovered at most three different consumption patterns for a single building and concluded that water use was different during work free days. Abu-Bakar, Williams and Hallett [113] discover four distinct water consumption patterns through K-means clustering and KNN classification. K-means clustering was applied after determining the optimum number o clusters (k) using the elbow method.…”

Contextualising household water consumption patterns in England: A socio-economic and socio-demographic narrative

“…In a recent empirical study (henceforth referred to as Study A), Abu-Bakar et al (2021b) used medium resolution smart meter data to characterise the consumption behaviours of 10,000 households in England. The study identified four clusters whose significant differences are defined by the distinction in the daily consumption patterns underpinning their peak demand.…”