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dc.contributor.authorSiqueira, T. G.-
dc.contributor.authorVillalva, M. G.-
dc.contributor.authorGazoli, J. R.-
dc.contributor.authorSalgado, R. M.-
dc.date.accessioned2014-05-27T11:27:25Z-
dc.date.accessioned2016-10-25T18:40:39Z-
dc.date.available2014-05-27T11:27:25Z-
dc.date.available2016-10-25T18:40:39Z-
dc.date.issued2012-12-11-
dc.identifierhttp://dx.doi.org/10.1109/PESGM.2012.6345492-
dc.identifier.citationIEEE Power and Energy Society General Meeting.-
dc.identifier.issn1944-9925-
dc.identifier.issn1944-9933-
dc.identifier.urihttp://hdl.handle.net/11449/74065-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/74065-
dc.description.abstractThe medium term hydropower scheduling (MTHS) problem involves an attempt to determine, for each time stage of the planning period, the amount of generation at each hydro plant which will maximize the expected future benefits throughout the planning period, while respecting plant operational constraints. Besides, it is important to emphasize that this decision-making has been done based mainly on inflow earliness knowledge. To perform the forecast of a determinate basin, it is possible to use some intelligent computational approaches. In this paper one considers the Dynamic Programming (DP) with the inflows given by their average values, thus turning the problem into a deterministic one which the solution can be obtained by deterministic DP (DDP). The performance of the DDP technique in the MTHS problem was assessed by simulation using the ensemble prediction models. Features and sensitivities of these models are discussed. © 2012 IEEE.en
dc.language.isoeng-
dc.sourceScopus-
dc.subjectArtificial Intelligence-
dc.subjectDynamic Programming-
dc.subjectEnsembles-
dc.subjectInflow Forecast-
dc.subjectMedium Term Hydropower Scheduling-
dc.subjectPredictive Models-
dc.subjectAverage values-
dc.subjectComputational approach-
dc.subjectEnsemble models-
dc.subjectEnsemble prediction-
dc.subjectFuture benefits-
dc.subjectHydro plants-
dc.subjectHydropower scheduling-
dc.subjectInflow forecast-
dc.subjectMedium term-
dc.subjectOperational constraints-
dc.subjectPlanning period-
dc.subjectPredictive models-
dc.subjectArtificial intelligence-
dc.subjectDynamic programming-
dc.titleAnalysis of ensemble models in the medium term hydropower schedulingen
dc.typeoutro-
dc.contributor.institutionFederal University of Alfenas-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)-
dc.contributor.institutionUniversity of Alfenas-
dc.description.affiliationScience and Technology Institute Federal University of Alfenas, Poços de Caldas, 37715-400-
dc.description.affiliationGroup of Automation and Integrated Systems Universidade Estadual Paulista, Sorocaba, SP, 18087-180-
dc.description.affiliationDepartment of Energy Control and Systems University of Campinas, Campinas, SP, 13083-852-
dc.description.affiliationInstitute of Exact Sciences University of Alfenas, Alfenas-
dc.description.affiliationUnespGroup of Automation and Integrated Systems Universidade Estadual Paulista, Sorocaba, SP, 18087-180-
dc.identifier.doi10.1109/PESGM.2012.6345492-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofIEEE Power and Energy Society General Meeting-
dc.identifier.scopus2-s2.0-84870591456-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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