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Correlation analysis of water demand and predictive variables for short-term forecasting models

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Correlation analysis of water demand and predictive variables for short-term forecasting models

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dc.contributor.author Brentan, B. M. es_ES
dc.contributor.author Meirelles, G. es_ES
dc.contributor.author Herrera Fernández, Antonio Manuel es_ES
dc.contributor.author Luvizotto, E., Jr. es_ES
dc.contributor.author Izquierdo Sebastián, Joaquín es_ES
dc.date.accessioned 2018-06-01T04:19:13Z
dc.date.available 2018-06-01T04:19:13Z
dc.date.issued 2017 es_ES
dc.identifier.issn 1024-123X es_ES
dc.identifier.uri http://hdl.handle.net/10251/103123
dc.description.abstract [EN] Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs) management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA) and machine learning powerful algorithms such as Self-Organizing Maps (SOMs) and Random Forest (RF). We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs) from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model. es_ES
dc.language Inglés es_ES
dc.publisher Hindawi Limited es_ES
dc.relation.ispartof Mathematical Problems in Engineering es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Correlation analysis of water demand and predictive variables for short-term forecasting models es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1155/2017/6343625 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 Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Brentan, BM.; Meirelles, G.; Herrera Fernández, AM.; Luvizotto, EJ.; Izquierdo Sebastián, J. (2017). Correlation analysis of water demand and predictive variables for short-term forecasting models. Mathematical Problems in Engineering. (6343625):1-10. doi:10.1155/2017/6343625 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1155/2017/6343625 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 10 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.issue 6343625 es_ES
dc.relation.pasarela S\349059 es_ES


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