Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.9/3722 |
Resumo: | ABSTRACT: Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture. |
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Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower GenerationEnergy modelingMachine learningHydropower generationABSTRACT: Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture.MDPIRepositório do LNEGSessa, ValentinaBossy, MireilleSimoes, Sofia2022-01-25T12:31:57Z2021-12-01T00:00:00Z2021-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.9/3722engSessa, Valentina... [et.al.] - Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation. In: Clean Technologies, 2021, Vol. 3, pp.858-88010.3390/cleantechnol30400502571-8797info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-09-06T12:29:28Zoai:repositorio.lneg.pt:10400.9/3722Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T15:36:56.341783Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation |
title |
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation |
spellingShingle |
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation Sessa, Valentina Energy modeling Machine learning Hydropower generation |
title_short |
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation |
title_full |
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation |
title_fullStr |
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation |
title_full_unstemmed |
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation |
title_sort |
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation |
author |
Sessa, Valentina |
author_facet |
Sessa, Valentina Bossy, Mireille Simoes, Sofia |
author_role |
author |
author2 |
Bossy, Mireille Simoes, Sofia |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório do LNEG |
dc.contributor.author.fl_str_mv |
Sessa, Valentina Bossy, Mireille Simoes, Sofia |
dc.subject.por.fl_str_mv |
Energy modeling Machine learning Hydropower generation |
topic |
Energy modeling Machine learning Hydropower generation |
description |
ABSTRACT: Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-01T00:00:00Z 2021-12-01T00:00:00Z 2022-01-25T12:31:57Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.9/3722 |
url |
http://hdl.handle.net/10400.9/3722 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sessa, Valentina... [et.al.] - Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation. In: Clean Technologies, 2021, Vol. 3, pp.858-880 10.3390/cleantechnol3040050 2571-8797 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799130235958984704 |