Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)

Detalhes bibliográficos
Ano de defesa: 2016
Autor(a) principal: Melo, Francisco Evandro de
Orientador(a): Carvalho, Paulo Cesar Marques de
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/15925
Resumo: The use of solar resource, as a supplementary source in the country’s energy matrix, requires a good forecast strategy that enables decision-making and strategic actions to keep the electrical potential generated in the national energy grid stable. Based on such premise, this dissertation presents the characterization and forecast of data series on solar irradiation registered at three locations in the Northeastern region of Brazil: from August 2012 to July 2013 in Parnaíba (PI), from May 2012 to April 2013 in Maracanaú (CE), and from May 2012 to March 2013 in Petrolina (PE). Those surveys consist of time series and, therefore, they require specific statistical methods for their own treatment and forecast. As seasonality is a characteristic found in time series data of solar irradiation, the characterization and forecast are made by using the low seasonality component of solar radiation: the atmospheric transparency index, known as Kt. The use of this component is justified for providing more accurate and reliable forecast results, with low interference from trend components present in time series data within the forecast process. The forecasts made in this study have used the ARIMA method, the Simple Exponential Smoothing method (SES), and the Moving Average (MA) method. Forecasts are obtained from a 30-day trial period for each location, adapted graphically between the dry season, and validated within periods of 30, 150 and 180 days in the rainy season. Then, the evaluation of the forecast methods used is done, being the ARIMA method a 30-day forecast and in need of validation within 30 days, which shows the lowest error rates, with Root Mean Squared Error for Prediction values (RMSEP) of 0.008 for Parnaíba (PI), 0.015 for Maracanaú (CE), and 0.010 for Petrolina (PE). Also, through the regression equations, by transforming the Kt values obtained with the ARIMA method forecast, one obtains a 30-day forecast of daily solar irradiation with averages of 6.4 kW/m² for Parnaíba, 5.69 kW/m² for Maracanaú, and 6.54 kW/m² for Petrolina
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spelling Melo, Francisco Evandro deCarvalho, Paulo Cesar Marques de2016-03-31T17:26:52Z2016-03-31T17:26:52Z2016MELO, F. E. Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE). 2016. 103 f. Dissertação (Mestrado em Engenharia Elétrica)-Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2016.http://www.repositorio.ufc.br/handle/riufc/15925The use of solar resource, as a supplementary source in the country’s energy matrix, requires a good forecast strategy that enables decision-making and strategic actions to keep the electrical potential generated in the national energy grid stable. Based on such premise, this dissertation presents the characterization and forecast of data series on solar irradiation registered at three locations in the Northeastern region of Brazil: from August 2012 to July 2013 in Parnaíba (PI), from May 2012 to April 2013 in Maracanaú (CE), and from May 2012 to March 2013 in Petrolina (PE). Those surveys consist of time series and, therefore, they require specific statistical methods for their own treatment and forecast. As seasonality is a characteristic found in time series data of solar irradiation, the characterization and forecast are made by using the low seasonality component of solar radiation: the atmospheric transparency index, known as Kt. The use of this component is justified for providing more accurate and reliable forecast results, with low interference from trend components present in time series data within the forecast process. The forecasts made in this study have used the ARIMA method, the Simple Exponential Smoothing method (SES), and the Moving Average (MA) method. Forecasts are obtained from a 30-day trial period for each location, adapted graphically between the dry season, and validated within periods of 30, 150 and 180 days in the rainy season. Then, the evaluation of the forecast methods used is done, being the ARIMA method a 30-day forecast and in need of validation within 30 days, which shows the lowest error rates, with Root Mean Squared Error for Prediction values (RMSEP) of 0.008 for Parnaíba (PI), 0.015 for Maracanaú (CE), and 0.010 for Petrolina (PE). Also, through the regression equations, by transforming the Kt values obtained with the ARIMA method forecast, one obtains a 30-day forecast of daily solar irradiation with averages of 6.4 kW/m² for Parnaíba, 5.69 kW/m² for Maracanaú, and 6.54 kW/m² for PetrolinaO uso do recurso solar como fonte complementar em uma matriz energética requer uma boa estratégia de previsão pois como a variância da irradiação solar causa uma variação na potência elétrica produzida, se faz necessário prever resultados que possibilitem garantir decisões e ações estratégicas que mantenham o recurso solar estável na malha energética. Dentro desta premissa, a presente dissertação apresenta a caracterização e previsão de séries de dados de irradiação solar, registradas nos períodos de agosto de 2012 a julho de 2013, em Parnaíba (PI), maio de 2012 a abril 2013, em Maracanaú (CE) e maio de 2012 a março de 2013, em Petrolina (PE). Estes levantamentos constituem-se como séries temporais e, portanto, para suas previsões, necessitam de métodos estatísticos específicos para o seu tratamento. Como a sazonalidade é uma característica presente em dados de séries temporais de irradiação solar, a caracterização e previsão são feitas utilizando a componente de baixa sazonalidade da irradiação solar, o índice de transparência atmosférica, Kt. O uso desta componente justifica-se pelo fato de propiciar resultados de previsões mais precisos e confiáveis, com baixa interferência das componentes de tendências, presentes nas séries de dados temporais, no processo de previsão. As previsões realizadas neste estudo utilizam o método ARIMA, o método de Alisamento Exponencial na modalidade Simples (AES) e o método de Médias Móveis (MA). Com adaptação gráfica dos dados e do método de previsão entre a estação seca e validação do modelo (calibração) em períodos de 30, 150 e 180 dias, na estação chuvosa, obtêm-se prognósticos de um período experimental de 30 dias para cada localidade. É realizada então a avaliação dos métodos de previsão utilizados, sendo o método ARIMA com previsão de 30 dias e validação do modelo em 30 dias, o que apresenta os menores índices de erro, com valores de Erro Quadrático Médio de Previsão (EQMP) de 0,008 para Parnaíba (PI), 0,015 para Maracanaú (CE) e 0,010 para Petrolina (PE). Através das equações de regressão, transformasse os valores de Kt obtidos com a previsão do método ARIMA, obtém-se as previsões de 30 dias de médias diárias de irradiação solar de 6,4 kWh/m² para Parnaíba, 5,69 kWh/m² para Maracanaú e 6,54 kWh/m² para PetrolinaEngenharia elétricaRadiação solarAnálise de séries temporaisCaracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)Characterization and forecasting of solar potential: a case study to Parnaíba (PI) Maracanaú (CE) and Petrolina (PE)info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisporreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFCinfo:eu-repo/semantics/openAccessORIGINAL2016_dis_femelo.pdf2016_dis_femelo.pdfapplication/pdf3887810http://repositorio.ufc.br/bitstream/riufc/15925/1/2016_dis_femelo.pdfd1342bf9ac5660a1724e7a01f8822135MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81786http://repositorio.ufc.br/bitstream/riufc/15925/2/license.txt8c4401d3d14722a7ca2d07c782a1aab3MD52riufc/159252020-11-06 10:09:48.836oai:repositorio.ufc.br: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Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2020-11-06T13:09:48Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false
dc.title.pt_BR.fl_str_mv Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)
dc.title.en.pt_BR.fl_str_mv Characterization and forecasting of solar potential: a case study to Parnaíba (PI) Maracanaú (CE) and Petrolina (PE)
title Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)
spellingShingle Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)
Melo, Francisco Evandro de
Engenharia elétrica
Radiação solar
Análise de séries temporais
title_short Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)
title_full Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)
title_fullStr Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)
title_full_unstemmed Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)
title_sort Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE)
author Melo, Francisco Evandro de
author_facet Melo, Francisco Evandro de
author_role author
dc.contributor.author.fl_str_mv Melo, Francisco Evandro de
dc.contributor.advisor1.fl_str_mv Carvalho, Paulo Cesar Marques de
contributor_str_mv Carvalho, Paulo Cesar Marques de
dc.subject.por.fl_str_mv Engenharia elétrica
Radiação solar
Análise de séries temporais
topic Engenharia elétrica
Radiação solar
Análise de séries temporais
description The use of solar resource, as a supplementary source in the country’s energy matrix, requires a good forecast strategy that enables decision-making and strategic actions to keep the electrical potential generated in the national energy grid stable. Based on such premise, this dissertation presents the characterization and forecast of data series on solar irradiation registered at three locations in the Northeastern region of Brazil: from August 2012 to July 2013 in Parnaíba (PI), from May 2012 to April 2013 in Maracanaú (CE), and from May 2012 to March 2013 in Petrolina (PE). Those surveys consist of time series and, therefore, they require specific statistical methods for their own treatment and forecast. As seasonality is a characteristic found in time series data of solar irradiation, the characterization and forecast are made by using the low seasonality component of solar radiation: the atmospheric transparency index, known as Kt. The use of this component is justified for providing more accurate and reliable forecast results, with low interference from trend components present in time series data within the forecast process. The forecasts made in this study have used the ARIMA method, the Simple Exponential Smoothing method (SES), and the Moving Average (MA) method. Forecasts are obtained from a 30-day trial period for each location, adapted graphically between the dry season, and validated within periods of 30, 150 and 180 days in the rainy season. Then, the evaluation of the forecast methods used is done, being the ARIMA method a 30-day forecast and in need of validation within 30 days, which shows the lowest error rates, with Root Mean Squared Error for Prediction values (RMSEP) of 0.008 for Parnaíba (PI), 0.015 for Maracanaú (CE), and 0.010 for Petrolina (PE). Also, through the regression equations, by transforming the Kt values obtained with the ARIMA method forecast, one obtains a 30-day forecast of daily solar irradiation with averages of 6.4 kW/m² for Parnaíba, 5.69 kW/m² for Maracanaú, and 6.54 kW/m² for Petrolina
publishDate 2016
dc.date.accessioned.fl_str_mv 2016-03-31T17:26:52Z
dc.date.available.fl_str_mv 2016-03-31T17:26:52Z
dc.date.issued.fl_str_mv 2016
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.citation.fl_str_mv MELO, F. E. Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE). 2016. 103 f. Dissertação (Mestrado em Engenharia Elétrica)-Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2016.
dc.identifier.uri.fl_str_mv http://www.repositorio.ufc.br/handle/riufc/15925
identifier_str_mv MELO, F. E. Caracterização e previsão de potencial solar: estudo de caso para Parnaíba (PI), Maracanaú (CE) e Petrolina (PE). 2016. 103 f. Dissertação (Mestrado em Engenharia Elétrica)-Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2016.
url http://www.repositorio.ufc.br/handle/riufc/15925
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