Flow time series clustering for demand pattern recognition in drinking water distribution systems: New insights about the most adequate methods

This study presents a proposal of clustering methodologies for demand pattern recognition using network flow data collected from a large set of drinking water distribution networks in Portugal. Most of the existing studies about clustering in flow time series rely on hierarchical or k-Means clusteri...

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Bibliographic Details
Main Author: Gomes, Pedro André Fonseca Garez (author)
Format: masterThesis
Language:eng
Published: 2019
Subjects:
Unsupervised learning
Time series clustering
Flow time series
Demand pattern recognition
Water distribution systems
Aprendizagem não supervisionada
Clustering de series temporais
Séries temporais de caudal
Reconhecimento de padrões de consumo
Sistemas de distribuição de água
Algoritmo > Algorithm
Clusters > Clusters
Séries temporais
Utilização da água
Portugal
Online Access:http://hdl.handle.net/10071/20195
Country:Portugal
Oai:oai:repositorio.iscte-iul.pt:10071/20195

Internet

http://hdl.handle.net/10071/20195

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