Analysis of ensemble models in the medium term hydropower scheduling
Autor(a) principal: | |
---|---|
Data de Publicação: | 2012 |
Outros Autores: | , , |
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. |
id |
UNSP_b4041342d69678b03c0b96a15ae698ac |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/74065 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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 |
|
_version_ |
1808129293424787456 |