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Combined Holt-Winters and GA trained ANN approach for sensor validation and reconstruction: application to water demand flowmeters
dc.contributor.author | Rodríguez Rangel, Héctor |
dc.contributor.author | Puig Cayuela, Vicenç |
dc.contributor.author | Flores, Juan J. |
dc.contributor.author | López, Rodrigo |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2017-03-10T11:35:21Z |
dc.date.available | 2017-03-10T11:35:21Z |
dc.date.issued | 2016 |
dc.identifier.citation | Rodríguez, H., Puig, V., Flores, J., López, R. Combined Holt-Winters and GA trained ANN approach for sensor validation and reconstruction: application to water demand flowmeters. A: International Conference on Control and Fault-Tolerant Systems. "SYSTOL 2016 - 3rd Conference on Control and Fault-Tolerant Systems, Barcelona, Spain, Sept. 7-9, 2016, proceedings book". Barcelona: IEEE Press, 2016, p. 196-201. |
dc.identifier.isbn | 978-1-5090-0658-8 |
dc.identifier.uri | http://hdl.handle.net/2117/102286 |
dc.description | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
dc.description.abstract | This paper proposes a Double Seasonal Holt-Winters (DSHW) forecasting model with an auxiliary Artificial Neural Network (ANN) trained with a Genetic Algorithm (GA) to model the DSHW residuals. ANN complements and improves the DSHW prediction. The proposed model also includes an on-line validation and reconstruction mechanism useful to detect and correct missing and erroneous data. This mechanism also impacts improving the DSHW prediction accuracy and precision. The proposed model and validation mechanism are applied to predict the time series generated by two monitored flowmeters of two sectors of Barcelona's drinking water network (DWN). The accuracy and precision improvement of the proposed method with respect to standard DSHW and ARIMA approaches is provided. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | IEEE Press |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
dc.subject.lcsh | Predictive control |
dc.subject.other | Predictive models |
dc.subject.other | Time series analysis |
dc.subject.other | Genetic algorithms |
dc.subject.other | Water resources |
dc.subject.other | Computational modeling |
dc.subject.other | Data models |
dc.subject.other | Training |
dc.title | Combined Holt-Winters and GA trained ANN approach for sensor validation and reconstruction: application to water demand flowmeters |
dc.type | Conference report |
dc.subject.lemac | Control predictiu |
dc.contributor.group | Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
dc.identifier.doi | 10.1109/SYSTOL.2016.7739751 |
dc.relation.publisherversion | http://ieeexplore.ieee.org/document/7739751/ |
dc.rights.access | Open Access |
local.identifier.drac | 19706038 |
dc.description.version | Accepted version |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//DPI2013-48243-C2-1-R/ES/OPERACION EFICIENTE DE INFRAESTRUCTURAS CRITICAS/ |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//DPI2014-58104-R/ES/CONTROL BASADO EN LA SALUD Y LA RESILIENCIA DE INFRAESTRUCTURAS CRITICAS Y SISTEMAS COMPLEJOS/ |
local.citation.author | Rodríguez, H.; Puig, V.; Flores, J.; López, R. |
local.citation.contributor | International Conference on Control and Fault-Tolerant Systems |
local.citation.pubplace | Barcelona |
local.citation.publicationName | SYSTOL 2016 - 3rd Conference on Control and Fault-Tolerant Systems, Barcelona, Spain, Sept. 7-9, 2016, proceedings book |
local.citation.startingPage | 196 |
local.citation.endingPage | 201 |