Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil

Detalhes bibliográficos
Autor(a) principal: SANTANA, Lêda Valéria Ramos
Data de Publicação: 2018
Tipo de documento: Tese
Idioma: por
Título da fonte: Biblioteca Digital de Teses e Dissertações da UFRPE
Texto Completo: http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7240
Resumo: The use of renewable energy sources has grown worldwide and in Brazil the incentive to use wind energy has been expanding significantly. Currently, the country stands out in the global wind scenario occupying the ninth position among the countries with largest installed capacity of accumulated wind power, leading the Latin American market. The Northeast (NE) has the highest wind potential in the country. However, there is a great variability in wind speed due to climatic diversity. The NE is divided into four sub-regions, Litoral, Agreste, Sertão e Meio Norte. There are different institutions that provide wind speed data, essential variable to study, related to deployment and wind power generation. This study analyzed wind speed time series coming from two different database, INMET and ERA-40. The records provided by INMET were obtained by conventional stations at 10 m above ground during the period from 1961 to 2001 at 00, 12 and 18h. The historical series of ERA-40 belong to a global grid with spatial resolution of 2.5 × 2.5 records at 10 m above ground at 00, 06, 12, 18h during the period from 1957 to 2001. The objective is to study spatial and temporal variability of wind speed and to measure the degree of similarity between the conventional stations (INMET) and reanalysis (ERA-40) database in order to quantify the degree of regularity of the time series and the degree of similarity between two time series using the methods Sample Entropy and cross-Sample Entropy of information theory. Due to lack of information on the basis of INMET, analyzes were performed on eight years of simultaneous data (1993 to 2000) to the database INMET and ERA-40, for the series of 00h, 12h, Full/Total (original series) and Daily (average per day). The results show that the highest wind speed records for different series are in the North of the four sub-regions. The Sample Entropy showed highest wind speed regularity in Meio Norte, where the wind speed is lower, with better predictability. The cross-Sample Entropy showed that there is moderate synchronization between the series INMET and ERA-40, indicating an overestimation or underestimation of ERA-40 data in relation to INMET data. The Meio Norte is the region with highest similarity between INMET and ERA-40 series. Examining the regularity of ERA-40 through all the observations from 1957 to 2001 using the Sample Entropy, it was found that the regions with higher wind speed have better predictability of wind speed temporal series, and that different regions with similar mean wind speed can have a different predictability.
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spelling FERREIRA, Tiago Alessandro EspínolaSTOSIC, TatijanaSTOSIC, TatijanaCUNHA FILHO, MoacyrMOURA, Geber Barbosa de AlbuquerqueMATTOS NETO, Paulo Salgado Gomes dehttp://lattes.cnpq.br/4805861067899137SANTANA, Lêda Valéria Ramos2018-05-10T13:18:47Z2018-02-20SANTANA, Lêda Valéria Ramos. Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil. 2018. 75 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife.http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7240The use of renewable energy sources has grown worldwide and in Brazil the incentive to use wind energy has been expanding significantly. Currently, the country stands out in the global wind scenario occupying the ninth position among the countries with largest installed capacity of accumulated wind power, leading the Latin American market. The Northeast (NE) has the highest wind potential in the country. However, there is a great variability in wind speed due to climatic diversity. The NE is divided into four sub-regions, Litoral, Agreste, Sertão e Meio Norte. There are different institutions that provide wind speed data, essential variable to study, related to deployment and wind power generation. This study analyzed wind speed time series coming from two different database, INMET and ERA-40. The records provided by INMET were obtained by conventional stations at 10 m above ground during the period from 1961 to 2001 at 00, 12 and 18h. The historical series of ERA-40 belong to a global grid with spatial resolution of 2.5 × 2.5 records at 10 m above ground at 00, 06, 12, 18h during the period from 1957 to 2001. The objective is to study spatial and temporal variability of wind speed and to measure the degree of similarity between the conventional stations (INMET) and reanalysis (ERA-40) database in order to quantify the degree of regularity of the time series and the degree of similarity between two time series using the methods Sample Entropy and cross-Sample Entropy of information theory. Due to lack of information on the basis of INMET, analyzes were performed on eight years of simultaneous data (1993 to 2000) to the database INMET and ERA-40, for the series of 00h, 12h, Full/Total (original series) and Daily (average per day). The results show that the highest wind speed records for different series are in the North of the four sub-regions. The Sample Entropy showed highest wind speed regularity in Meio Norte, where the wind speed is lower, with better predictability. The cross-Sample Entropy showed that there is moderate synchronization between the series INMET and ERA-40, indicating an overestimation or underestimation of ERA-40 data in relation to INMET data. The Meio Norte is the region with highest similarity between INMET and ERA-40 series. Examining the regularity of ERA-40 through all the observations from 1957 to 2001 using the Sample Entropy, it was found that the regions with higher wind speed have better predictability of wind speed temporal series, and that different regions with similar mean wind speed can have a different predictability.O uso de fontes renováveis tem crescido mundialmente e, no Brasil o incentivo ao uso de energia eólica tem sido ampliado de forma expressiva. Atualmente, o país destaca-se no cenário eólico mundial ocupando a nona posição entre os países que tiveram maiores capacidades instaladas acumuladas de energia eólica, liderando o mercado Latino-Americano. A região Nordeste (NE) detém o maior potencial eólico do país. Todavia, apresentando grande variedade na velocidade do vento devido a diversidade climática. A extensa área está dividida em quatro sub-regiões, Litoral, Agreste, Sertão e Meio Norte. Existem diferentes instituições que disponibilizam dados da velocidade do vento, variável essencial para estudo relacionados à implantação e geração de energia eólica, sobre o NE. Neste trabalho foram analisadas séries históricas de velocidade do vento oriundas de duas bases de dados distintas, Instituto Nacional de Meteorologia (INMET) e ECMWF Re-Analyses (ERA-40). Os registros disponibilizados pelo INMET foram obtidos por meio de estações convencionais instaladas a 10 m do solo durante o período de 1961 a 2001, às 00, 12 e 18h. As séries históricas da ERA-40 pertencem a uma grade mundial com resolução espacial de 2, 5 × 2, 5 em registros realizados a 10 m do solo durante as 00, 06, 12, 18h durante o período de 1957 a 2001. O objetivo é estudar a variabilidade espaço temporal da velocidade do vento e medir o grau de similaridade entre as bases de dados de estações convencionais (INMET) e de reanálise (ERA-40), afim de quantificar o grau de regularidade da série temporal e o grau de similaridade entre duas séries temporais, utilizando os métodos Sample Entropy e cross-Sample Entropy da teoria da informação. Devido a falta de informações na base do INMET, as análises foram realizadas no período de oito anos de dados simultâneos (1993 a 2000) para a base de dados do INMET e da ERA-40, para as séries as 00h, 12h, Completa/Total (série original), Diária (média por dia), para . Os resultados mostram que os maiores registros de velocidade do vento para diferentes séries encontram-se no Norte das quatro sub-regiões. A Sample Entropy, apresentou maior regularidade da velocidade do vento no Meio Norte, área onde a velocidade do vento é menor, apresentando melhor previsibilidade nesta área. A cross-Sample Entropy mostrou uma sincronização moderada das séries do INMET e ERA-40, indicando uma superestimação ou subestimação dos dados da ERA-40 em relação aos dados do INMET. O Meio Norte representa também, a região com melhor similaridade entre as séries do INMET e ERA-40. Ao analisar a regularidade da ERA-40 por meio de todas as observações do período de 1957 a 2001 utilizando a Sample Entropy, foi possível identificar que as regiões de maior velocidade do vento também apresentam melhor previsibilidade para as séries. Além de, indicar que áreas distintas com comportamento das médias da velocidade do vento semelhantes podem apresentar previsibilidade diferentes.Submitted by Mario BC (mario@bc.ufrpe.br) on 2018-05-10T13:18:47Z No. of bitstreams: 1 Leda Valeria Ramos Santana.pdf: 3207517 bytes, checksum: 6862902d5ff1ca2d7e21877edefa8a9e (MD5)Made available in DSpace on 2018-05-10T13:18:47Z (GMT). No. of bitstreams: 1 Leda Valeria Ramos Santana.pdf: 3207517 bytes, checksum: 6862902d5ff1ca2d7e21877edefa8a9e (MD5) Previous issue date: 2018-02-20Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfporUniversidade Federal Rural de PernambucoPrograma de Pós-Graduação em Biometria e Estatística AplicadaUFRPEBrasilDepartamento de Estatística e InformáticaVelocidade do ventoSérie temporalRegião Nordeste (BR)CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICAAnálise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis768382242446187918600600600600-6774555140396120501-58364078281851435172075167498588264571info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFRPEinstname:Universidade Federal Rural de Pernambuco (UFRPE)instacron:UFRPEORIGINALLeda Valeria Ramos Santana.pdfLeda Valeria Ramos Santana.pdfapplication/pdf3207517http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/7240/2/Leda+Valeria+Ramos+Santana.pdf6862902d5ff1ca2d7e21877edefa8a9eMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/7240/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede2/72402018-05-10 10:18:47.309oai:tede2: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Biblioteca Digital de Teses e Dissertaçõeshttp://www.tede2.ufrpe.br:8080/tede/PUBhttp://www.tede2.ufrpe.br:8080/oai/requestbdtd@ufrpe.br ||bdtd@ufrpe.bropendoar:2024-05-28T12:35:23.521451Biblioteca Digital de Teses e Dissertações da UFRPE - Universidade Federal Rural de Pernambuco (UFRPE)false
dc.title.por.fl_str_mv Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil
title Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil
spellingShingle Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil
SANTANA, Lêda Valéria Ramos
Velocidade do vento
Série temporal
Região Nordeste (BR)
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
title_short Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil
title_full Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil
title_fullStr Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil
title_full_unstemmed Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil
title_sort Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil
author SANTANA, Lêda Valéria Ramos
author_facet SANTANA, Lêda Valéria Ramos
author_role author
dc.contributor.advisor1.fl_str_mv FERREIRA, Tiago Alessandro Espínola
dc.contributor.advisor-co1.fl_str_mv STOSIC, Tatijana
dc.contributor.referee1.fl_str_mv STOSIC, Tatijana
dc.contributor.referee2.fl_str_mv CUNHA FILHO, Moacyr
dc.contributor.referee3.fl_str_mv MOURA, Geber Barbosa de Albuquerque
dc.contributor.referee4.fl_str_mv MATTOS NETO, Paulo Salgado Gomes de
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/4805861067899137
dc.contributor.author.fl_str_mv SANTANA, Lêda Valéria Ramos
contributor_str_mv FERREIRA, Tiago Alessandro Espínola
STOSIC, Tatijana
STOSIC, Tatijana
CUNHA FILHO, Moacyr
MOURA, Geber Barbosa de Albuquerque
MATTOS NETO, Paulo Salgado Gomes de
dc.subject.por.fl_str_mv Velocidade do vento
Série temporal
Região Nordeste (BR)
topic Velocidade do vento
Série temporal
Região Nordeste (BR)
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
dc.subject.cnpq.fl_str_mv CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
description The use of renewable energy sources has grown worldwide and in Brazil the incentive to use wind energy has been expanding significantly. Currently, the country stands out in the global wind scenario occupying the ninth position among the countries with largest installed capacity of accumulated wind power, leading the Latin American market. The Northeast (NE) has the highest wind potential in the country. However, there is a great variability in wind speed due to climatic diversity. The NE is divided into four sub-regions, Litoral, Agreste, Sertão e Meio Norte. There are different institutions that provide wind speed data, essential variable to study, related to deployment and wind power generation. This study analyzed wind speed time series coming from two different database, INMET and ERA-40. The records provided by INMET were obtained by conventional stations at 10 m above ground during the period from 1961 to 2001 at 00, 12 and 18h. The historical series of ERA-40 belong to a global grid with spatial resolution of 2.5 × 2.5 records at 10 m above ground at 00, 06, 12, 18h during the period from 1957 to 2001. The objective is to study spatial and temporal variability of wind speed and to measure the degree of similarity between the conventional stations (INMET) and reanalysis (ERA-40) database in order to quantify the degree of regularity of the time series and the degree of similarity between two time series using the methods Sample Entropy and cross-Sample Entropy of information theory. Due to lack of information on the basis of INMET, analyzes were performed on eight years of simultaneous data (1993 to 2000) to the database INMET and ERA-40, for the series of 00h, 12h, Full/Total (original series) and Daily (average per day). The results show that the highest wind speed records for different series are in the North of the four sub-regions. The Sample Entropy showed highest wind speed regularity in Meio Norte, where the wind speed is lower, with better predictability. The cross-Sample Entropy showed that there is moderate synchronization between the series INMET and ERA-40, indicating an overestimation or underestimation of ERA-40 data in relation to INMET data. The Meio Norte is the region with highest similarity between INMET and ERA-40 series. Examining the regularity of ERA-40 through all the observations from 1957 to 2001 using the Sample Entropy, it was found that the regions with higher wind speed have better predictability of wind speed temporal series, and that different regions with similar mean wind speed can have a different predictability.
publishDate 2018
dc.date.accessioned.fl_str_mv 2018-05-10T13:18:47Z
dc.date.issued.fl_str_mv 2018-02-20
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
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dc.identifier.citation.fl_str_mv SANTANA, Lêda Valéria Ramos. Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil. 2018. 75 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife.
dc.identifier.uri.fl_str_mv http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7240
identifier_str_mv SANTANA, Lêda Valéria Ramos. Análise da variabilidade e similaridade da velocidade do vento no Nordeste do Brasil. 2018. 75 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife.
url http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7240
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dc.publisher.program.fl_str_mv Programa de Pós-Graduação em Biometria e Estatística Aplicada
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dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv Departamento de Estatística e Informática
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