Water utilities increasingly require a deeper understanding of users' water demand to optimize resource management and meet customer needs. The adoption of smart metering solutions enables the collection of detailed consumption data. This study presents a methodology for identifying usage patterns by analysing smart meter data. In particular, the authors introduce a machine learning methodology to identify user profiles from smart meter water consumption data. Clustering results are graphically interpreted and compared, providing insights into consumption habits and assessing the methodology's effectiveness and stability.
SMART METER DATA ANALYSIS: MODELLING AND CLUSTERING PATTERNS IN WATER DISTRIBUTION SYSTEMS
Mariaelena Berlotti
;Sarah Di Grande;Salvatore Cavalieri;
2025-01-01
Abstract
Water utilities increasingly require a deeper understanding of users' water demand to optimize resource management and meet customer needs. The adoption of smart metering solutions enables the collection of detailed consumption data. This study presents a methodology for identifying usage patterns by analysing smart meter data. In particular, the authors introduce a machine learning methodology to identify user profiles from smart meter water consumption data. Clustering results are graphically interpreted and compared, providing insights into consumption habits and assessing the methodology's effectiveness and stability.File in questo prodotto:
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