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@InProceedings{LimaPereMart:2015:FoShSo,
               author = "Lima, Francisco Jos{\'e} Lopes de and Pereira, Enio Bueno and 
                         Martins, Fernando Ramos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Forecast of short-term solar irradiation in Brazil using numerical 
                         models and statistical post-processing",
            booktitle = "Proceedings...",
                 year = "2015",
                pages = "1",
         organization = "EuroSun 2014.",
                 note = "{Setores de Atividade: Pesquisa e desenvolvimento 
                         cient{\'{\i}}fico.}",
             keywords = "Solar forecasting, Solar Energy, Solar Radiation.",
             abstract = "This work aims at establishing a methodology to get reliable solar 
                         irradiation forecasts for the Brazilian Northeastern region by 
                         using WRF model together with statistical methods for 
                         post-processing data. The key issue is concerning how to deal with 
                         the diversity of typical climate features occurring in the region 
                         presenting the largest solar energy resource in Brazil. The solar 
                         irradiance forecasts for 24h in advance were obtained using the 
                         WRF model. In order to reduce uncertainties, cluster analysis 
                         technique was employed to find out areas presenting similar 
                         climate features. Comparison analysis between WRF model outputs 
                         and observational data were performed to evaluate the model skill 
                         in forecasting the surface solar irradiation. After all, 
                         post-processing of WRF outputs were performed using artificial 
                         neural networks and multiple regression methods in order to refine 
                         short-term solar irradiation forecasts.",
  conference-location = "Aix-les-Bains",
      conference-year = "2014",
                  doi = "10.18086/eurosun.2014.09.01",
                  url = "http://dx.doi.org/10.18086/eurosun.2014.09.01",
                 isbn = "9783981465938",
                label = "lattes: 0638551133292550 2 LimaPereMart:2015:FoShSo",
             language = "en",
           targetfile = "1_lima.pdf",
                  url = "http://proceedings.ises.org/",
        urlaccessdate = "05 dez. 2020"
}


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