WangZhiwei,Khu Soon Thiam,WangWeijie,et al.Water Consumption Pattern Analysis of Residential Communities Based on K-means Clustering Algorithm[J].China Water & Wastewater,2026,42(15):47-53.
Water Consumption Pattern Analysis of Residential Communities Based on K-means Clustering Algorithm
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第42卷
Number:
第15期
Page:
47-53
Column:
Date of publication:
2026-08-01
- Keywords:
- water consumption pattern; K-means clustering algorithm; pipe network model; feature extraction
- Abstract:
- In the process of water supply network modeling, the accuracy of user water consumption pattern settings has a significant impact on the simulation accuracy of the network model. However, the water consumption patterns currently used in China are unable to reflect the actual water usage of different users at different times. Taking residential water use as an example, this paper proposed a method for extracting user water consumption patterns based on the K-means clustering algorithm. A total of 118 communities in Fuzhou were selected as the research subjects. The collected smart water meter data were preprocessed to obtain continuous water consumption time series. Dimensionality reduction of the water consumption curves was performed by extracting characteristic indicators of water use during peak periods. The K-means algorithm was then applied to cluster the reduced-dimension curves, and a compositional analysis of the clustering results was conducted to derive water consumption patterns for weekdays and weekends across different seasons. Compared with conventional water consumption pattern extraction methods, the curves obtained by the K-means approach can more accurately reflect the temporal characteristics of water use variations within communities, and can provide important data support for subsequent operation and management by water utilities.
Last Update:
2026-08-01