Multi-model prediction for demand forecast in water distribution networks

This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily co...

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Authors: López Farías, Rodrigo, Rodríguez-Rangel, Héctor, Puig, Vicenç, Flores, Juan J.
Format: article
Status:Published version
Publication Date:2018
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/179261
Online Access:http://hdl.handle.net/10261/179261
Access Level:Open access
Keyword:Prediction
Multi-model
Water demand
Short-term prediction
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spelling Multi-model prediction for demand forecast in water distribution networksLópez Farías, RodrigoRodríguez-Rangel, HéctorPuig, VicençFlores, Juan J.PredictionMulti-modelWater demandShort-term predictionThis paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracyThis work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Union through FEDER program through the projects DEOCS (ref. DPI2016-76493-C3-3-R) and HARCRICS (ref. DPI2014-58104-R).Peer reviewedMolecular Diversity Preservation InternationalMinisterio de Economía y Competitividad (España)European CommissionLópez Farías, Rodrigo [0000-0003-2772-0051]Rodriguez Rangel, Hector [0000-0003-4999-3472]Flores, Juan J. [0000-0002-0379-7495]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201920192018info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/179261reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2016-76493-C3-3-Rinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2014-58104-RSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1792612026-05-22T06:33:51Z
dc.title.none.fl_str_mv Multi-model prediction for demand forecast in water distribution networks
title Multi-model prediction for demand forecast in water distribution networks
spellingShingle Multi-model prediction for demand forecast in water distribution networks
López Farías, Rodrigo
Prediction
Multi-model
Water demand
Short-term prediction
title_short Multi-model prediction for demand forecast in water distribution networks
title_full Multi-model prediction for demand forecast in water distribution networks
title_fullStr Multi-model prediction for demand forecast in water distribution networks
title_full_unstemmed Multi-model prediction for demand forecast in water distribution networks
title_sort Multi-model prediction for demand forecast in water distribution networks
dc.creator.none.fl_str_mv López Farías, Rodrigo
Rodríguez-Rangel, Héctor
Puig, Vicenç
Flores, Juan J.
author López Farías, Rodrigo
author_facet López Farías, Rodrigo
Rodríguez-Rangel, Héctor
Puig, Vicenç
Flores, Juan J.
author_role author
author2 Rodríguez-Rangel, Héctor
Puig, Vicenç
Flores, Juan J.
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
European Commission
López Farías, Rodrigo [0000-0003-2772-0051]
Rodriguez Rangel, Hector [0000-0003-4999-3472]
Flores, Juan J. [0000-0002-0379-7495]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Prediction
Multi-model
Water demand
Short-term prediction
topic Prediction
Multi-model
Water demand
Short-term prediction
description This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy
publishDate 2018
dc.date.none.fl_str_mv 2018
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/179261
url http://hdl.handle.net/10261/179261
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2016-76493-C3-3-R
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2014-58104-R
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
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