Analysis of ensemble models in the medium term hydropower scheduling

Detalhes bibliográficos
Autor(a) principal: Siqueira, T. G.
Data de Publicação: 2012
Outros Autores: Villalva, M. G. [UNESP], Gazoli, J. R., Salgado, R. M.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/PESGM.2012.6345492
http://hdl.handle.net/11449/74065
Resumo: The 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.
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spelling Analysis of ensemble models in the medium term hydropower schedulingArtificial IntelligenceDynamic ProgrammingEnsemblesInflow ForecastMedium Term Hydropower SchedulingPredictive ModelsAverage valuesComputational approachEnsemble modelsEnsemble predictionFuture benefitsHydro plantsHydropower schedulingInflow forecastMedium termOperational constraintsPlanning periodPredictive modelsArtificial intelligenceDynamic programmingThe 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.Science and Technology Institute Federal University of Alfenas, Poços de Caldas, 37715-400Group of Automation and Integrated Systems Universidade Estadual Paulista, Sorocaba, SP, 18087-180Department of Energy Control and Systems University of Campinas, Campinas, SP, 13083-852Institute of Exact Sciences University of Alfenas, AlfenasGroup of Automation and Integrated Systems Universidade Estadual Paulista, Sorocaba, SP, 18087-180Federal University of AlfenasUniversidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)University of AlfenasSiqueira, T. G.Villalva, M. G. [UNESP]Gazoli, J. R.Salgado, R. M.2014-05-27T11:27:25Z2014-05-27T11:27:25Z2012-12-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PESGM.2012.6345492IEEE Power and Energy Society General Meeting.1944-99251944-9933http://hdl.handle.net/11449/7406510.1109/PESGM.2012.63454922-s2.0-84870591456Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Power and Energy Society General Meeting0,328info:eu-repo/semantics/openAccess2021-10-23T21:41:36Zoai:repositorio.unesp.br:11449/74065Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:10:31.350165Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Analysis of ensemble models in the medium term hydropower scheduling
title Analysis of ensemble models in the medium term hydropower scheduling
spellingShingle Analysis of ensemble models in the medium term hydropower scheduling
Siqueira, T. G.
Artificial Intelligence
Dynamic Programming
Ensembles
Inflow Forecast
Medium Term Hydropower Scheduling
Predictive Models
Average values
Computational approach
Ensemble models
Ensemble prediction
Future benefits
Hydro plants
Hydropower scheduling
Inflow forecast
Medium term
Operational constraints
Planning period
Predictive models
Artificial intelligence
Dynamic programming
title_short Analysis of ensemble models in the medium term hydropower scheduling
title_full Analysis of ensemble models in the medium term hydropower scheduling
title_fullStr Analysis of ensemble models in the medium term hydropower scheduling
title_full_unstemmed Analysis of ensemble models in the medium term hydropower scheduling
title_sort Analysis of ensemble models in the medium term hydropower scheduling
author Siqueira, T. G.
author_facet Siqueira, T. G.
Villalva, M. G. [UNESP]
Gazoli, J. R.
Salgado, R. M.
author_role author
author2 Villalva, M. G. [UNESP]
Gazoli, J. R.
Salgado, R. M.
author2_role author
author
author
dc.contributor.none.fl_str_mv Federal University of Alfenas
Universidade Estadual Paulista (Unesp)
Universidade Estadual de Campinas (UNICAMP)
University of Alfenas
dc.contributor.author.fl_str_mv Siqueira, T. G.
Villalva, M. G. [UNESP]
Gazoli, J. R.
Salgado, R. M.
dc.subject.por.fl_str_mv Artificial Intelligence
Dynamic Programming
Ensembles
Inflow Forecast
Medium Term Hydropower Scheduling
Predictive Models
Average values
Computational approach
Ensemble models
Ensemble prediction
Future benefits
Hydro plants
Hydropower scheduling
Inflow forecast
Medium term
Operational constraints
Planning period
Predictive models
Artificial intelligence
Dynamic programming
topic Artificial Intelligence
Dynamic Programming
Ensembles
Inflow Forecast
Medium Term Hydropower Scheduling
Predictive Models
Average values
Computational approach
Ensemble models
Ensemble prediction
Future benefits
Hydro plants
Hydropower scheduling
Inflow forecast
Medium term
Operational constraints
Planning period
Predictive models
Artificial intelligence
Dynamic programming
description The 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.
publishDate 2012
dc.date.none.fl_str_mv 2012-12-11
2014-05-27T11:27:25Z
2014-05-27T11:27:25Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/PESGM.2012.6345492
IEEE Power and Energy Society General Meeting.
1944-9925
1944-9933
http://hdl.handle.net/11449/74065
10.1109/PESGM.2012.6345492
2-s2.0-84870591456
url http://dx.doi.org/10.1109/PESGM.2012.6345492
http://hdl.handle.net/11449/74065
identifier_str_mv IEEE Power and Energy Society General Meeting.
1944-9925
1944-9933
10.1109/PESGM.2012.6345492
2-s2.0-84870591456
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv IEEE Power and Energy Society General Meeting
0,328
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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