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@PhDThesis{Lima:2015:PrIrSo,
               author = "Lima, Francisco Jos{\'e} Lopes de",
                title = "Previs{\~a}o de irradia{\c{c}}{\~a}o solar no nordeste do 
                         Brasil empregando o modelo WRF ajustado por redes neurais 
                         artificiais (RNAs)",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2015",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2015-06-10",
             keywords = "radia{\c{c}}{\~a}o solar, redes neurais artificiais, 
                         previs{\~a}o, an{\'a}lise de agrupamento, nordeste do Brasil, 
                         solar radiation, artificial neural networks, forecast, cluster 
                         analysis, northeast of Brazil.",
             abstract = "Este trabalho tem como objetivo avaliar a capacidade de um modelo 
                         num{\'e}rico regional de mesoescala, em representar o escoamento 
                         atmosf{\'e}rico da regi{\~a}o Nordeste do Brasil (NEB), 
                         possibilitando seu uso para previs{\~a}o de 
                         irradia{\c{c}}{\~a}o solar usando um refinamento 
                         estat{\'{\i}}stico para a redu{\c{c}}{\~a}o dos erros 
                         sistem{\'a}ticos inerente ao modelo de mesoescala. A 
                         motiva{\c{c}}{\~a}o deste estudo decorre da import{\^a}ncia da 
                         radia{\c{c}}{\~a}o solar como recurso vital para a 
                         manuten{\c{c}}{\~a}o da vida no planeta e para atividades 
                         humanas tais como agricultura e aproveitamento de energia. A 
                         intensidade da irradia{\c{c}}{\~a}o solar que incide sobre a 
                         superf{\'{\i}}cie {\'e} de natureza vari{\'a}vel, 
                         principalmente devido {\`a}s nuvens e o ciclo diurno. Este 
                         trabalho se prop{\^o}s a desenvolver uma metodologia para 
                         previs{\~a}o de irradia{\c{c}}{\~a}o solar incidente para a 
                         regi{\~a}o Nordeste do Brasil com o uso de Redes Neurais 
                         Artificiais (RNAs), alimentadas por sa{\'{\i}}das do modelo WRF, 
                         visando reduzir as incertezas associadas {\`a} previs{\~a}o de 
                         irradia{\c{c}}{\~a}o solar deste modelo. As vari{\'a}veis de 
                         sa{\'{\i}}da do modelo WRF, representando as 
                         condi{\c{c}}{\~o}es atmosf{\'e}ricas previstas, foram 
                         empregadas como preditores em modelos de RNAs e Regress{\~o}es 
                         Lineares M{\'u}ltiplas (RLM). O m{\'e}todo de an{\'a}lise de 
                         cluster foi utilizado para estabelecer regi{\~o}es de 
                         caracter{\'{\i}}sticas homog{\^e}neas sob o ponto de vista 
                         climatol{\'o}gico da irradia{\c{c}}{\~a}o solar. Os dados 
                         usados neste trabalho foram dados do INMET, para o 
                         per{\'{\i}}odo de sete anos de 2005 a 2011. Diversos 
                         experimentos foram realizados para ajuste e defini{\c{c}}{\~a}o 
                         de preditores e simula{\c{c}}{\~a}o dos modelos de RNAs. 
                         Par{\^a}metros de avalia{\c{c}}{\~a}o de erros, determinados 
                         frente aos dados observacionais de cada esta{\c{c}}{\~a}o de 
                         coleta de dados em superf{\'{\i}}cie foram calculados, 
                         permitindo a compara{\c{c}}{\~a}o de desempenho das RNA e RLM e 
                         da previs{\~a}o de irradia{\c{c}}{\~a}o solar obtida 
                         diretamente do modelo WRF. Visando maximizar o ganho de desempenho 
                         sobre o modelo WRF e minimizar o n{\'u}mero de vari{\'a}veis, 
                         encontrou-se a melhor arquitetura e um grupo de 10 preditores, com 
                         o qual an{\'a}lises mais aprofundadas foram realizadas, incluindo 
                         avalia{\c{c}}{\~a}o de desempenho para o outono e primavera de 
                         2011, per{\'{\i}}odo chuvoso e seco no NEB, principalmente no 
                         norte do NEB. Houve uma diferen{\c{c}}a significativa entre os 
                         modelos de RNA e RLM, mostrando que os modelos de RNAs foram 
                         superiores ao modelo RLM. Por{\'e}m ambos os m{\'e}todos 
                         promoveram redu{\c{c}}{\~a}o do vi{\'e}s e do \emph{RMSE} e 
                         aumento do coeficiente de correla{\c{c}}{\~a}o em 
                         compara{\c{c}}{\~a}o com as sa{\'{\i}}das de 
                         irradia{\c{c}}{\~a}o solar do WRF. ABSTRACT: This work aims to 
                         evaluate the ability of a regional numerical mesoscale model, to 
                         represent the atmospheric flow in the Northeast region of Brazil 
                         (NEB), allowing its use for forecasting solar irradiation using a 
                         statistical refinement to reduce systematic errors inherent in the 
                         mesoscale model. The motivation of this study lies in the 
                         importance of solar radiation as a vital resource for the 
                         maintenance of life on Earth and to human activities such as 
                         agriculture and energy. The intensity of the solar radiation 
                         incident on the surface is variable in nature, mainly because of 
                         clouds and the diurnal cycle. This study aimed to develop a 
                         methodology to forecast the incident surface solar irradiation in 
                         the Northeast of Brazil by using mesoscale WRF model outputs 
                         adjusted by Artificial Neural Networks (ANN to reduce the model 
                         uncertainties. The output variables of the WRF model, representing 
                         the forecast atmospheric conditions, were used as predictors by 
                         RNAs and Multiple Linear Regressions (MLR), (with the inclusion of 
                         a clearsky model), adjusted to calculate the incident solar 
                         irradiation, in four homogeneous regions defined by the Wards 
                         method. The data used in this study cover the period of seven 
                         years from 2005 to 2011. Several predictors were tested in the 
                         adjustment and simulation of the ANN. Error evaluation parameters, 
                         determined by the observational data of each measurement station 
                         were calculated for each simulation, allowing the comparison of 
                         RNA and RLM, and the prediction of solar irradiation directly from 
                         WRF model. To maximize the performance gain of the WRF model and 
                         minimize the number of variables, it was establish the best 
                         architecture and a group of 10 predictors, with which more 
                         in-depth analyzes were performed, including performance evaluation 
                         for fall and spring of 2011 (rainy and dry season at the NEB, 
                         mainly in North of the Northeast). There was a significant 
                         difference between RNA and MLR models, showing that RNA models 
                         were superior to the MLR model. However, both methods produced 
                         lower bias and RMSE, and an increase in the correlation 
                         coefficient in comparison with the solar radiation in the WRF 
                         Model.",
            committee = "Ceballos, Juan Carlos (presidente) and Pereira, Enio Bueno 
                         (orientador) and Sansigolo, Cl{\'o}vis Angeli and Martins, 
                         Fernando Ramos and Ruther, Ricardo",
           copyholder = "SID/SCD",
         englishtitle = "Forecast of solar irradiation in northeast of Brazil using the WRF 
                         model adjusted by artificial neural network (ANN)",
             language = "pt",
                pages = "250",
                  ibi = "8JMKD3MGP8W/3JH3BSE",
                  url = "http://urlib.net/rep/8JMKD3MGP8W/3JH3BSE",
           targetfile = "publicacao.pdf",
        urlaccessdate = "24 nov. 2020"
}


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