Generating Water Demand Scenarios Using Scaling Laws

Detalhes bibliográficos
Autor(a) principal: Vertommen, I.
Data de Publicação: 2014
Outros Autores: Magini, R., Cunha, M. da Conceição
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/46677
https://doi.org/10.1016/j.proeng.2014.02.187
Resumo: This paper addresses uncertainty inherent to water demand and proposes an approach to generate demand scenarios and calculate their probability of occurrence. Nodal water demands are modelled as correlated stochastic variables. The parameters which characterize demand vary with spatial and temporal aggregation levels. Scaling laws allow the definition of these parameters for different users and sampling rates. Different scenarios are generated by considering different combinations of demands at each node of the network. A multivariate normal distribution is used to obtain the probability of each demand scenario. Correlation between demands is found to significantly affect the scenarios probabilities.
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spelling Generating Water Demand Scenarios Using Scaling LawsScalingwater demanddistribution networksrobust optimizationscenariosThis paper addresses uncertainty inherent to water demand and proposes an approach to generate demand scenarios and calculate their probability of occurrence. Nodal water demands are modelled as correlated stochastic variables. The parameters which characterize demand vary with spatial and temporal aggregation levels. Scaling laws allow the definition of these parameters for different users and sampling rates. Different scenarios are generated by considering different combinations of demands at each node of the network. A multivariate normal distribution is used to obtain the probability of each demand scenario. Correlation between demands is found to significantly affect the scenarios probabilities.Elsevier2014info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/46677http://hdl.handle.net/10316/46677https://doi.org/10.1016/j.proeng.2014.02.187enghttps://www.sciencedirect.com/science/article/pii/S1877705814001891Vertommen, I.Magini, R.Cunha, M. da Conceiçãoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2021-11-09T09:37:23Zoai:estudogeral.uc.pt:10316/46677Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:57:18.224816Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Generating Water Demand Scenarios Using Scaling Laws
title Generating Water Demand Scenarios Using Scaling Laws
spellingShingle Generating Water Demand Scenarios Using Scaling Laws
Vertommen, I.
Scaling
water demand
distribution networks
robust optimization
scenarios
title_short Generating Water Demand Scenarios Using Scaling Laws
title_full Generating Water Demand Scenarios Using Scaling Laws
title_fullStr Generating Water Demand Scenarios Using Scaling Laws
title_full_unstemmed Generating Water Demand Scenarios Using Scaling Laws
title_sort Generating Water Demand Scenarios Using Scaling Laws
author Vertommen, I.
author_facet Vertommen, I.
Magini, R.
Cunha, M. da Conceição
author_role author
author2 Magini, R.
Cunha, M. da Conceição
author2_role author
author
dc.contributor.author.fl_str_mv Vertommen, I.
Magini, R.
Cunha, M. da Conceição
dc.subject.por.fl_str_mv Scaling
water demand
distribution networks
robust optimization
scenarios
topic Scaling
water demand
distribution networks
robust optimization
scenarios
description This paper addresses uncertainty inherent to water demand and proposes an approach to generate demand scenarios and calculate their probability of occurrence. Nodal water demands are modelled as correlated stochastic variables. The parameters which characterize demand vary with spatial and temporal aggregation levels. Scaling laws allow the definition of these parameters for different users and sampling rates. Different scenarios are generated by considering different combinations of demands at each node of the network. A multivariate normal distribution is used to obtain the probability of each demand scenario. Correlation between demands is found to significantly affect the scenarios probabilities.
publishDate 2014
dc.date.none.fl_str_mv 2014
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/46677
http://hdl.handle.net/10316/46677
https://doi.org/10.1016/j.proeng.2014.02.187
url http://hdl.handle.net/10316/46677
https://doi.org/10.1016/j.proeng.2014.02.187
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S1877705814001891
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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