@TechReport{MartinsCereMantWang:2021:SyLiRe,
               author = "Martins, Bruno Juncklaus and Cerentini, Allan and Mantelli Neto, 
                         Sylvio Luiz and von Wangenheim, Aldo",
                title = "Systematic Literature Review on Forecasting/Nowcasting based upon 
                         Ground-Based Cloud Imaging",
          institution = "Instituto Nacional de Pesquisas Espaciais",
                 year = "2021",
                 type = "RPQ",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Artificial neural networks, forecasting, cloud imaging.",
             abstract = "Artificial Neural Networks (ANN) are being used on several fields 
                         mostly as a mapper from input domain variables into output 
                         application area results. Several methods are being used on the 
                         automatic assessment of clouds from surface to predict solar power 
                         generation, assisted by a camera, side sensors, etc. The present 
                         Systematic Literature Review (SLR) is intended to search the 
                         related scientific articles, to find the state of the art in the 
                         area. We were able to find gaps in researches in regards to 
                         validation metrics for prediction of solar power generation as 
                         well as a small number of works in this domain area using 
                         computational intelligence (machine learning) methods, with the 
                         majority of works relying on classical statistics approaches. 
                         Results show that most works rely on images captured by Total 
                         Sky-imagers (TSI) and most works using computational intelligence 
                         rely on classical approaches like Artificial Neural Networks, 
                         Convolutional Neural Networks (CNN) and Multilayer Perceptrons 
                         (MLP) and that there still a relevant amount of works published 
                         from the last three years using classical statistics.",
          affiliation = "{Universidade Federal de Santa Catarina (UFSC)} and {Universidade 
                         Federal de Santa Catarina (UFSC)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade Federal de Santa 
                         Catarina (UFSC)}",
             language = "en",
                pages = "64",
                  ibi = "8JMKD3MGP3W34R/44CUAMH",
                  url = "http://urlib.net/ibi/8JMKD3MGP3W34R/44CUAMH",
           targetfile = "Martins_systematic.pdf",
        urlaccessdate = "10 maio 2024"
}