A clustering solution for analyzing residential water consumption patterns

作者：

Highlights：

• Discussed a detailed methodology to analyse water consumption data for clustering.

• Performed a comprehensive experimental study on the data and clustering methods.

• Identified the most suitable profiling interval in terms of similarity among the items in each cluster.

• Observed how tap-water usage can determine hand-hygiene practices during COVID-19 like scenarios.

摘要

•Discussed a detailed methodology to analyse water consumption data for clustering.•Performed a comprehensive experimental study on the data and clustering methods.•Identified the most suitable profiling interval in terms of similarity among the items in each cluster.•Observed how tap-water usage can determine hand-hygiene practices during COVID-19 like scenarios.

论文关键词：Digital water meters,Residential water consumption,Clustering,Customer segmentation,k-means clustering,Hierarchical agglomerative clustering,Consumption patterns,Data analytics,Machine learning

论文评审过程：Received 10 November 2020, Revised 16 September 2021, Accepted 18 September 2021, Available online 30 September 2021, Version of Record 18 October 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107522