Uncovering urban water consumption patterns through time series clustering and entropy analysis

Renfang Wang, Xinyu Zhao, Hong Qiu*, Xu Cheng, Xiufeng Liu

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Sustainable urban water management is crucial for meeting the growing demands of urban populations. This study presents a novel approach that combines time series clustering, seasonal analysis, and entropy analysis to uncover residential water consumption patterns and their drivers. Using a three-year dataset from the SmartH2o project, encompassing 374 households, we identify nine distinct water consumption patterns through time series clustering, leveraging Dynamic Time Warping (DTW) as the optimal similarity measure. Multiple linear regression reveals key household characteristics influencing water usage behaviors, such as the number of bathrooms and appliance efficiency ratings. Seasonal analysis uncovers temporal dynamics, highlighting shifts towards lower consumption during summer months and increased variability in transitional seasons. Entropy analysis quantifies the diversity and complexity of water consumption at both cluster and household levels, informing targeted interventions. This comprehensive, granular approach enables the development of personalized water conservation strategies and policies, empowering water utilities to optimize resource management and contribute to sustainable urban water practices.

Original languageEnglish
Article number122085
JournalWater Research
Volume262
ISSN0043-1354
DOIs
Publication statusPublished - 2024

Keywords

  • Entropy analysis
  • Household water consumption
  • Time series clustering
  • Urban water management
  • Water conservation

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