Short-term forecasting of hourly water demands: a Portuguese case study
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/27579 |
Resumo: | Predicting future water demands is becoming essential for the efficient management of water supply systems (WSS). To improve the operations of a Portuguese network, short-term water demand forecasting models are applied to a number of datasets collected from distinct locations in the network. Traditional forecasting models, such as exponential smoothing and naïve models, and artificial neural network (ANN)-based models are developed and compared. Additionally, the influence of anthropic and weather variables in the ANN-based models is also analysed. Results demonstrate that, for this case-study, ANN-based models outperform the traditional models when external predictors such as anthropic and weather variables are included in the models. However, the inappropriate choice of such variables may lead to worse forecasting performances. |
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Short-term forecasting of hourly water demands: a Portuguese case studyWater demand forecastingArtificial Neural NetworksData analysisExponential SmoothingNaïve methodsPortuguese water networkPredicting future water demands is becoming essential for the efficient management of water supply systems (WSS). To improve the operations of a Portuguese network, short-term water demand forecasting models are applied to a number of datasets collected from distinct locations in the network. Traditional forecasting models, such as exponential smoothing and naïve models, and artificial neural network (ANN)-based models are developed and compared. Additionally, the influence of anthropic and weather variables in the ANN-based models is also analysed. Results demonstrate that, for this case-study, ANN-based models outperform the traditional models when external predictors such as anthropic and weather variables are included in the models. However, the inappropriate choice of such variables may lead to worse forecasting performances.Inderscience2020-05-02T00:00:00Z2019-05-02T00:00:00Z2019-05-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/27579eng1465-662010.1504/IJW.2019.099515Coelho, BernardeteAndrade-Campos, Antónioinfo: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:RCAAP2024-02-22T11:53:23Zoai:ria.ua.pt:10773/27579Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:00:18.230434Repositório 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 |
Short-term forecasting of hourly water demands: a Portuguese case study |
title |
Short-term forecasting of hourly water demands: a Portuguese case study |
spellingShingle |
Short-term forecasting of hourly water demands: a Portuguese case study Coelho, Bernardete Water demand forecasting Artificial Neural Networks Data analysis Exponential Smoothing Naïve methods Portuguese water network |
title_short |
Short-term forecasting of hourly water demands: a Portuguese case study |
title_full |
Short-term forecasting of hourly water demands: a Portuguese case study |
title_fullStr |
Short-term forecasting of hourly water demands: a Portuguese case study |
title_full_unstemmed |
Short-term forecasting of hourly water demands: a Portuguese case study |
title_sort |
Short-term forecasting of hourly water demands: a Portuguese case study |
author |
Coelho, Bernardete |
author_facet |
Coelho, Bernardete Andrade-Campos, António |
author_role |
author |
author2 |
Andrade-Campos, António |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Coelho, Bernardete Andrade-Campos, António |
dc.subject.por.fl_str_mv |
Water demand forecasting Artificial Neural Networks Data analysis Exponential Smoothing Naïve methods Portuguese water network |
topic |
Water demand forecasting Artificial Neural Networks Data analysis Exponential Smoothing Naïve methods Portuguese water network |
description |
Predicting future water demands is becoming essential for the efficient management of water supply systems (WSS). To improve the operations of a Portuguese network, short-term water demand forecasting models are applied to a number of datasets collected from distinct locations in the network. Traditional forecasting models, such as exponential smoothing and naïve models, and artificial neural network (ANN)-based models are developed and compared. Additionally, the influence of anthropic and weather variables in the ANN-based models is also analysed. Results demonstrate that, for this case-study, ANN-based models outperform the traditional models when external predictors such as anthropic and weather variables are included in the models. However, the inappropriate choice of such variables may lead to worse forecasting performances. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-05-02T00:00:00Z 2019-05-02 2020-05-02T00:00:00Z |
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/10773/27579 |
url |
http://hdl.handle.net/10773/27579 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1465-6620 10.1504/IJW.2019.099515 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Inderscience |
publisher.none.fl_str_mv |
Inderscience |
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 instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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 |
|
_version_ |
1799137658530693120 |