- -

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

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

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

Show simple item record

Files in this item

dc.contributor.author Brentan, Bruno M. es_ES
dc.contributor.author Luvizotto, E. es_ES
dc.contributor.author Herrera Fernández, Antonio Manuel es_ES
dc.contributor.author Izquierdo Sebastián, Joaquín es_ES
dc.contributor.author Pérez García, Rafael es_ES
dc.date.accessioned 2018-07-16T06:47:45Z
dc.date.available 2018-07-16T06:47:45Z
dc.date.issued 2017 es_ES
dc.identifier.issn 0377-0427 es_ES
dc.identifier.uri http://hdl.handle.net/10251/105819
dc.description.abstract [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. es_ES
dc.description.sponsorship 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.
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Journal of Computational and Applied Mathematics es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Demand forecasting es_ES
dc.subject Water supply es_ES
dc.subject Fourier series es_ES
dc.subject Support vector regression es_ES
dc.subject Near real-time algorithms es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Hybrid regression model for near real-time urban water demand forecasting es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1016/j.cam.2016.02.009 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Brentan, BM.; Luvizotto, E.; Herrera Fernández, AM.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics. 309:532-541. doi:10.1016/j.cam.2016.02.009 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename Mathematical Modelling in Engineering & Human Behaviour 2015. 17th Edition of the Mathematical Modelling Conference Series at the Institute for Multidisciplinary Mathematics es_ES
dc.relation.conferencedate Septiembre 09-11,2015 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.relation.publisherversion http://doi.org/10.1016/j.cam.2016.02.009 es_ES
dc.description.upvformatpinicio 532 es_ES
dc.description.upvformatpfin 541 es_ES
dc.type.version info:eu repo/semantics/publishedVersion es_ES
dc.description.volume 309 es_ES
dc.relation.pasarela 302393 es_ES


This item appears in the following Collection(s)

Show simple item record