Modelling and Clustering Patterns from Smart Meter Data in Water Distribution Systems

In recent years, water utilities have increasingly required a deeper understanding of users’ water demand across their distribution networks to optimize resource management and meet customers' needs. With the adoption of smart metering solutions, it has become possible to investigate water usage at a finer resolution, enabling the collection of more detailed consumption data. In the present study, the authors present an innovative methodology for identifying water usage using data from smart meters. First, a Multiple Seasonal- Trend Decomposition algorithm is applied to extract seasonality from the raw time-series data. Next, the Bootstrap sampling technique is used to train an optimized Time Series K-means algorithm on multiple data configurations. Finally, the clustering results are interpreted graphically and validated, providing valuable insights into consumption habits and a comprehensive assessment of the methodology's effectiveness and stability.