Exploring the Effectiveness of Clustering Algorithms for Capturing Water Consumption Behavior at Household Level

Alexandra E. Ioannou, Enrico F. Creaco and Chrysi S. Laspidou
Additional contact information
Alexandra E. Ioannou: Civil Engineering Department, University of Thessaly, 38334 Volos, Greece
Enrico F. Creaco: Dipartimento di Ingegneria Civile e Architettura, Università degli Studi di Pavia, via Ferrata 3, 27100 Pavia, Italy
Chrysi S. Laspidou: Civil Engineering Department, University of Thessaly, 38334 Volos, Greece

Sustainability, 2021, vol. 13, issue 5, 1-15

Abstract: As water scarcity becomes more prevalent, the analysis of urban water consumption patterns at the consumer level and the estimation of the corresponding water demand for water utility are expected to be among the top priorities of water companies in the near future. This study proposes a comprehensive methodology for water managers to achieve an efficient operation of urban water networks, by successfully detecting residential water consumption patterns corresponding to different household needs and behaviors. The methodology uses Self Organizing Maps as the main clustering algorithm in combination with K-means and Hierarchical Agglomerative Clustering. The objective is to create clusters in a literature dataset that includes water consumption from 21 customers located in Milford, Ohio, USA, for a 7-month period. Originally, water consumption data was recorded for every water use incident in the household, while for this analysis, the information is converted to half-hourly water consumption. Individual customers with similar consumption behavior are clustered and water-consumption curves are calculated for each cluster; these curves can be used by the water utility to obtain estimates of the spatio-temporal distribution of demand, thus giving insight into peak demands at different locations. Statistical indices of agreement are used to confirm a good agreement between the estimated and observed water use, when clustering is employed. The resulting curves show a clear improvement in capturing water consumption behavior at household level, when compared to corresponding curves obtained without clustering. This analysis offers water utilities an innovative solution that relies on real time data and uses data science principles for optimizing water supply and network operation and provides tools for the efficient use of water resources.

Keywords: self-organizing maps; time-series clustering; household water consumption; data science; K-means; Hierarchical Agglomerative Clustering; smart cities; behavioral change (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/5/2603/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/5/2603/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:5:p:2603-:d:508138

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().