@InProceedings{Muralikrishna:2018:MoPiPr,
               author = "Muralikrishna, Amita",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "A modular pipeline proposal for the short-term solar irradiance 
                         forecasting",
            booktitle = "Anais...",
                 year = "2018",
         organization = "IAU General Assembly, 30.",
             abstract = "he Sun, being a star closer to the Earth, allows its more detailed 
                         and constant monitoring, which benefits the study of the causes 
                         and consequences of its influence on the planet as well as the 
                         study of the profile and behavior of other stars of the Universe. 
                         Among several aspects that can be investigated in solar activity, 
                         the relation of a given solar cycle and its features - such as the 
                         formation and evolution of sunspots and active regions - with 
                         events occurring on Earth can contribute to the achievement of 
                         short-term and long-term researches around the terrestrial climate 
                         and the disturbances of the Earth atmosphere layers. One of the 
                         relevant parameters of this relationship is the total and spectral 
                         solar irradiance, which provides an indication of how much the 
                         radiation emitted by the star can influence life on the planet. 
                         The accurate prediction of solar irradiance at the top of the 
                         Earth's atmosphere, using for that solar activity data, has been a 
                         challenge, since the orbiting instruments that perform these 
                         measurements have a limited useful life and suffer damages over 
                         time. This work proposes the modularization of the irradiance 
                         forecast task, suggesting a process composed of steps that can 
                         become relatively independent, so that they can be replaced by 
                         other options like another database or computational techniques 
                         for specific tasks, for example, thus offering flexibility for the 
                         continuous optimization of the process. The aim is to construct 
                         the process in the form of a pipeline, using some of Data Science 
                         concepts, such as reproducibility and application of Machine 
                         Learning techniques, together with functionalities that involve 
                         traceability of partial and final results and their availability 
                         as products.",
  conference-location = "Vienna, Austria",
      conference-year = "20-31 aug.",
             language = "pt",
        urlaccessdate = "04 maio 2024"
}