Keywords

user profiling; user segmentation; principal component analysis; water de- mand management; machine learning

Location

Session C5: ICT for Energy and Water Demand Management

Start Date

13-7-2016 11:10 AM

End Date

13-7-2016 11:30 AM

Abstract

Developing effective demand-side management strategies is essential to meet future residential water demands, pursue water conservation, and reduce the costs for water utilities. The effectiveness of water demand management strategies relies on our understanding of water consumers’ behavior and their consumption habits and routines, which can be monitored through the deployment of smart metering technologies and the adoption of data analytics and machine learning techniques. This work contributes a novel modeling procedure, based on a combination of clustering and principal component analysis, which allows performing water users’ segmentation on the basis of their eigenbehaviors (i.e., recurrent water consumption behaviors) automatically identified from smart metered consumption data. The approach is tested against a dataset of smart metered water consumption data from 175 households in the municipality of Tegna (CH). Numerical results demonstrate the potential of the method for identifying typical profiles of water consumption, which constitute essential information to support residential water demand management.

 
Jul 13th, 11:10 AM Jul 13th, 11:30 AM

Profiling residential water users’ routines by eigenbehavior modelling

Session C5: ICT for Energy and Water Demand Management

Developing effective demand-side management strategies is essential to meet future residential water demands, pursue water conservation, and reduce the costs for water utilities. The effectiveness of water demand management strategies relies on our understanding of water consumers’ behavior and their consumption habits and routines, which can be monitored through the deployment of smart metering technologies and the adoption of data analytics and machine learning techniques. This work contributes a novel modeling procedure, based on a combination of clustering and principal component analysis, which allows performing water users’ segmentation on the basis of their eigenbehaviors (i.e., recurrent water consumption behaviors) automatically identified from smart metered consumption data. The approach is tested against a dataset of smart metered water consumption data from 175 households in the municipality of Tegna (CH). Numerical results demonstrate the potential of the method for identifying typical profiles of water consumption, which constitute essential information to support residential water demand management.