Hybrid regression model for near real-time urban water demand forecasting

[EN] The most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of sta...

ver descrição completa

Detalhes bibliográficos
Autores: Brentan, Bruno M., Luvizotto, E., Herrera Fernández, Antonio Manuel, Pérez García, Rafael, Izquierdo Sebastián, Joaquín|||0000-0002-6625-7226
Formato: artículo
Fecha de publicación:2017
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/105819
Acesso em linha:https://riunet.upv.es/handle/10251/105819
Access Level:acceso abierto
Palavra-chave:Demand forecasting
Water supply
Fourier series
Support vector regression
Near real-time algorithms
MATEMATICA APLICADA
INGENIERIA HIDRAULICA
id ES_3a3a33d8bf93abfc9dfea73315888ca2
oai_identifier_str oai:riunet.upv.es:10251/105819
network_acronym_str ES
network_name_str España
repository_id_str
spelling Hybrid regression model for near real-time urban water demand forecastingBrentan, Bruno M.Luvizotto, E.Herrera Fernández, Antonio ManuelPérez García, RafaelIzquierdo Sebastián, Joaquín|||0000-0002-6625-7226Demand forecastingWater supplyFourier seriesSupport vector regressionNear real-time algorithmsMATEMATICA APLICADAINGENIERIA HIDRAULICA[EN] The most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of statistical, machine learning, and artificial intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement; and new challenges regarding on-line predictive models for water demand have appeared. This work proposes applying support vector regression, as one of the currently better machine learning options for short-term water demand forecasting, to build a base prediction. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes. (C) 2016 Elsevier B.V. All rights reserved.This work has been partially supported by CAPES Foundation of Brazil’s Ministry of Education. The data were provided by SABESP, São Paulo state water management company.ElsevierEscuela Técnica Superior de Ingeniería de TelecomunicaciónDepartamento de Matemática AplicadaInstituto Universitario de Matemática MultidisciplinarCoordenaçao de Aperfeiçoamento de Pessoal de Nível Superior, BrasilRepositorio Institucional de la Universitat Politècnica de València Riunet20172017-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/105819reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1058192026-06-13T07:49:27Z
dc.title.none.fl_str_mv Hybrid regression model for near real-time urban water demand forecasting
title Hybrid regression model for near real-time urban water demand forecasting
spellingShingle Hybrid regression model for near real-time urban water demand forecasting
Brentan, Bruno M.
Demand forecasting
Water supply
Fourier series
Support vector regression
Near real-time algorithms
MATEMATICA APLICADA
INGENIERIA HIDRAULICA
title_short Hybrid regression model for near real-time urban water demand forecasting
title_full Hybrid regression model for near real-time urban water demand forecasting
title_fullStr Hybrid regression model for near real-time urban water demand forecasting
title_full_unstemmed Hybrid regression model for near real-time urban water demand forecasting
title_sort Hybrid regression model for near real-time urban water demand forecasting
dc.creator.none.fl_str_mv Brentan, Bruno M.
Luvizotto, E.
Herrera Fernández, Antonio Manuel
Pérez García, Rafael
Izquierdo Sebastián, Joaquín|||0000-0002-6625-7226
author Brentan, Bruno M.
author_facet Brentan, Bruno M.
Luvizotto, E.
Herrera Fernández, Antonio Manuel
Pérez García, Rafael
Izquierdo Sebastián, Joaquín|||0000-0002-6625-7226
author_role author
author2 Luvizotto, E.
Herrera Fernández, Antonio Manuel
Pérez García, Rafael
Izquierdo Sebastián, Joaquín|||0000-0002-6625-7226
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Escuela Técnica Superior de Ingeniería de Telecomunicación
Departamento de Matemática Aplicada
Instituto Universitario de Matemática Multidisciplinar
Coordenaçao de Aperfeiçoamento de Pessoal de Nível Superior, Brasil
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Demand forecasting
Water supply
Fourier series
Support vector regression
Near real-time algorithms
MATEMATICA APLICADA
INGENIERIA HIDRAULICA
topic Demand forecasting
Water supply
Fourier series
Support vector regression
Near real-time algorithms
MATEMATICA APLICADA
INGENIERIA HIDRAULICA
description [EN] The most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of statistical, machine learning, and artificial intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement; and new challenges regarding on-line predictive models for water demand have appeared. This work proposes applying support vector regression, as one of the currently better machine learning options for short-term water demand forecasting, to build a base prediction. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes. (C) 2016 Elsevier B.V. All rights reserved.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/105819
url https://riunet.upv.es/handle/10251/105819
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406222024704000
score 15,301629

Registos relacionados