Identifying daily water consumption patterns based on K-means Clustering, Agglomerative Hierarchical Clustering, and Spectral Clustering algorithms

Understanding daily water consumption patterns is crucial for efficient management and distribution of water resources, as well as for promoting energy conservation and achieving carbon peaking and neutrality targets. It compares performance of three clustering algorithms, K-means Clustering (KC), A...

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Bibliographic Details
Published in:Aqua (London, England) Vol. 73; no. 5; pp. 870 - 887
Main Authors: Guo, Hongyuan, Liu, Xingpo, Zhang, Qichen
Format: Journal Article
Language:English
Published: Oxford IWA Publishing 01.05.2024
Subjects:
agglomerative hierarchical clustering
Algorithms
Artificial intelligence
Cluster analysis
Clustering
Consumption patterns
Daily
daily water consumption patterns
Data analysis
Energy conservation
Energy consumption
Energy efficiency
k-means clustering
spectral clustering
Vector quantization
Water consumption
Water distribution
Water engineering
Water resources
Water shortages
Water supply
ISSN:2709-8028, 1606-9935, 2709-8036, 1605-3974
Online Access:Get full text
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Summary:Understanding daily water consumption patterns is crucial for efficient management and distribution of water resources, as well as for promoting energy conservation and achieving carbon peaking and neutrality targets. It compares performance of three clustering algorithms, K-means Clustering (KC), Agglomerative Hierarchical Clustering (AHC), and Spectral Clustering (SC), using Silhouette Coefficient Index (SCI) and Calinski–Harabasz Index (CHI) as evaluation metrics. We conducted a case study using original hourly flow series of a water distribution division. It aims to identify typical daily water consumption patterns and explore factors that influence them. Findings are as follows: (1) among the three algorithms, KC demonstrates the best, with SCI of 0.6315, 0.5922, and 0.6272, and CHI of 305.9207, 274.1120, and 302.4738 for KC, AHC, and SC, respectively. (2) KC successfully identifies three distinct typical daily water consumption patterns. (3) Results indicate a significant impact of seasons on daily water consumption patterns. (4) Conversely, weekdays and holidays have minimal effect on daily water consumption patterns. It highlights the importance of comprehending daily water consumption patterns and underscores the effectiveness of KC in identifying such patterns. Furthermore, it emphasizes the significant influence of seasons while revealing limited impact of weekdays and holidays on daily water consumption patterns.
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ISSN:2709-8028
1606-9935
2709-8036
1605-3974
DOI:10.2166/aqua.2024.294