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@Article{LimaMarPerLorHei:2016:FoSuSo,
               author = "Lima, Francisco Jos{\'e} Lopes de and Martins, Fernando Ramos and 
                         Pereira, Enio Bueno and Lorenz, Elke and Heinemann, Detlev",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of 
                         Oldenburg} and {Universidade Federal de S{\~a}o Paulo 
                         (UNIFESP)}",
                title = "Forecast for surface solar irradiance at the Brazilian 
                         Northeastern region using NWP model and artificial neural 
                         networks",
              journal = "Renewable Energy",
                 year = "2016",
               volume = "87",
                pages = "807--818",
                month = "Mar.",
             keywords = "Artificial neural network, Solar energy forecast, Solar 
                         irradiance, WRF model.",
             abstract = "There has been a growing demand on energy sector for short-term 
                         predictions of energy resources to support the planning and 
                         management of electricity generation and distribution systems. The 
                         purpose of this work is establishing a methodology to produce 
                         solar irradiation forecasts for the Brazilian Northeastern region 
                         by using Weather Research and Forecasting Model (WRF) combined 
                         with a statistical post-processing method. The 24 h solar 
                         irradiance forecasts were obtained using the WRF model. In order 
                         to reduce uncertainties, a cluster analysis technique was employed 
                         to select areas presenting similar climate features. Comparison 
                         analysis between WRF model outputs and observational data were 
                         performed to evaluate the model skill in forecasting surface solar 
                         irradiance. Next, model-derived short-term solar irradiance 
                         forecasts from the WRF outputs were refined by using an artificial 
                         neural networks (ANNs) technique. The output variables of the WRF 
                         model representing the forecasted atmospheric conditions were used 
                         as predictors by ANNs, adjusted to calculate the solar radiation 
                         incident for the entire Brazilian Northeastern (NEB) (which was 
                         divided into four homogeneous regions, defined by the Ward 
                         method). The data used in this study was from rainy and dry 
                         seasons between 2009 and 2011. Several predictors were tested to 
                         adjust and simulate the ANNs. We found the best ANN architecture 
                         and a group of 10 predictors, in which a deeper analyzes were 
                         carried out, including performance evaluation for Fall and Spring 
                         of 2011 (rainy and dry season in NEB, mainly in the northern 
                         section). There was a significant improvement of the WRF model 
                         forecasts when adjusted by the ANNs, yielding lower bias and RMSE, 
                         and an increase in the correlation coefficient.",
                  doi = "10.1016/j.renene.2015.11.005",
                  url = "http://dx.doi.org/10.1016/j.renene.2015.11.005",
                 issn = "0960-1481",
             language = "en",
           targetfile = "Lima_forecast.pdf",
        urlaccessdate = "24 nov. 2020"
}


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