Analysis of ensemble models in the medium term hydropower scheduling

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dc.contributor.authorSiqueira, T. G.
dc.contributor.authorVillalva, M. G. [UNESP]
dc.contributor.authorGazoli, J. R.
dc.contributor.authorSalgado, R. M.
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.date.accessioned2014-05-27T11:27:25Z
dc.date.available2014-05-27T11:27:25Z
dc.date.issued2012-12-11
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.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.identifierhttp://dx.doi.org/10.1109/PESGM.2012.6345492
dc.identifier.citationIEEE Power and Energy Society General Meeting.
dc.identifier.doi10.1109/PESGM.2012.6345492
dc.identifier.issn1944-9925
dc.identifier.issn1944-9933
dc.identifier.scopus2-s2.0-84870591456
dc.identifier.urihttp://hdl.handle.net/11449/74065
dc.language.isoeng
dc.relation.ispartofIEEE Power and Energy Society General Meeting
dc.relation.ispartofsjr0,328
dc.rights.accessRightsAcesso aberto
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.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Ciência e Tecnologia, Sorocabapt

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