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@InProceedings{LimaCostMartPere:2015:AvPrRa,
               author = "Lima, Francisco Jos{\'e} Lopes de and Costa, Rodrigo Santos and 
                         Martins, Fernando R. and Pereira, Enio Bueno",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de S{\~a}o Paulo (UNIFESP)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Avalia{\c{c}}{\~a}o da previs{\~a}o de radia{\c{c}}{\~a}o com 
                         base em ferramentas de p{\'o}s-processamento aplicadas em 
                         simula{\c{c}}{\~o}es do modelo de mesoescala WRF",
            booktitle = "P{\^o}steres",
                 year = "2015",
         organization = "Simp{\'o}sio Internacional de Climatologia, 6. (SIC)",
             keywords = "Previs{\~a}o de radia{\c{c}}{\~a}o solar, WRF, Redes Neurais 
                         Artificiais, Regress{\~a}o Linear M{\'u}ltipla, Solar radiation 
                         forecast, WRF model, Artificial Neural Networks, Multiple Linear 
                         Regression.",
             abstract = "A previs{\~a}o de curto prazo da radia{\c{c}}{\~a}o solar 
                         incidente {\'e} uma quest{\~a}o importante para as 
                         aplica{\c{c}}{\~o}es deste recurso como fonte de energia. O uso 
                         de modelos num{\'e}ricos de mesoescala, combinados com 
                         ferramentas estat{\'{\i}}sticas de p{\'o}s-processamento podem 
                         aumentar a acur{\'a}cia das simula{\c{c}}{\~o}es de algumas 
                         horas ou mesmo de alguns dias. Neste sentido, apresenta-se a 
                         avalia{\c{c}}{\~a}o de um sistema de previs{\~a}o de 
                         irradia{\c{c}}{\~a}o solar de curto prazo, com base no modelo 
                         meteorol{\'o}gico de mesoescala WRF e em dois m{\'e}todos 
                         estat{\'{\i}}sticos de p{\'o}sprocessamento, a fim de melhorar 
                         o desempenho das estimativas. Foram avaliados resultados obtidos 
                         em simula{\c{c}}{\~o}es do ano de 2009 sobre o Nordeste 
                         Brasileiro (NEB) em dois per{\'{\i}}odos com 
                         caracter{\'{\i}}sticas clim{\'a}ticas distintas na regi{\~a}o, 
                         que s{\~a}o Outono e Primavera e, portanto considerando-se o 
                         per{\'{\i}}odo chuvoso e o per{\'{\i}}odo seco na maior parte 
                         da mesma. O modelo WRF foi integrado com um dom{\'{\i}}nio 
                         externo de resolu{\c{c}}{\~a}o horizontal de 15 km, cobrindo 
                         toda a regi{\~a}o Nordeste, e a partir da{\'{\i}} outros 
                         tr{\^e}s dom{\'{\i}}nios de resolu{\c{c}}{\~a}o horizontal de 
                         5 km foram aninhados. Os resultados das simula{\c{c}}{\~o}es 
                         foram comparados com dados de 121 esta{\c{c}}{\~o}es 
                         meteorol{\'o}gicas autom{\'a}ticas do Instituto Nacional de 
                         Meteorologia (INMET), indicando que o modelo WRF superestima a 
                         irradia{\c{c}}{\~a}o solar nos dois per{\'{\i}}odos simulados, 
                         mas com menores diferen{\c{c}}as no Outono (a hip{\'o}tese 
                         {\'e} a maior nebulosidade na regi{\~a}o). As t{\'e}cnicas de 
                         p{\'o}sprocessamento estat{\'{\i}}stico utilizadas foram as 
                         Redes Neurais Artificiais (RNA) e Regress{\~a}o Linear 
                         M{\'u}ltipla (RLM), que permitiram melhorias significativas nos 
                         resultados das simula{\c{c}}{\~o}es realizadas, verificados a 
                         partir da redu{\c{c}}{\~a}o do BIAS e do RMSE. Dentre estas, as 
                         RNA's tiveram desempenho superior {\`a}s RLM's. Estes resultados 
                         permitem uma an{\'a}lise da confiabilidade de sistemas similares 
                         de previs{\~a}o de irradi{\^a}ncia solar, em termos de sua 
                         disponibilidade de curto prazo e da estimativa da 
                         produ{\c{c}}{\~a}o de energia, indicando algumas melhorias que 
                         podem ser avaliadas e consequentemente implementadas no futuro. 
                         ABSTRACT: The short-term forecast of solar radiation is an 
                         important issue for the applications of this feature as energy 
                         source. The use of mesoscale numerical models combined with 
                         statistical post-processing tools can increase the accuracy of a 
                         few hours or even a few days simulations. In this way, this study 
                         presents the evaluation of a short-term prediction of solar 
                         irradiation system based on the WRF mesoscale meteorological model 
                         with two statistical post-processing technics, in order to improve 
                         the estimates performance. It was evaluated simulations from 2009 
                         on the Brazilian Northeast region (NEB), in two periods with 
                         different climatic characteristics, which are autumn and spring, 
                         and in this way considering the rainy season and the dry season. 
                         The WRF model was integrated with an external domain with 
                         horizontal resolution of 15 km, covering the entire NEB, and from 
                         there other three domains of horizontal resolution of 5 km were 
                         nested. The simulations results were compared with data of 121 
                         automatic weather stations of the National Institute of 
                         Meteorology (INMET), indicating that the WRF model overestimates 
                         the solar irradiation in the two simulated periods, but with minor 
                         differences in autumn (the hypothesis is this case is the 
                         cloudiness increment in region). The statistical post-processing 
                         techniques used were as Artificial Neural Networks (ANN) and 
                         Multiple Linear Regression (MLR), which allowed significant 
                         improvements in the simulations results, indicated by the 
                         reduction of BIAS and RMSE. However, the ANN's outperformed the 
                         MLR's. These results allows an analysis of the reliability of 
                         similar systems of solar irradiance forecast, in terms of his 
                         short term availability and energy production estimates, 
                         indicating some improvements that can be evaluated and 
                         consequently implemented in the future.",
  conference-location = "Natal, RN",
      conference-year = "13-16 out.",
        urlaccessdate = "05 dez. 2020"
}


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