Methodologies based on Genetic Algorithms and Numerical Linear Algebra for Missing Data Imputation in Observed Solar Radiation


Prediction of climatic variables, in particular, those related to wind and solar radiation, has developed a considerable interest in recent years, mainly due to its applications to Energy, Renewable Energy, and Agrometeorology. In many cases there is a large number of factors that influence the climatic variable of interest, and the researcher chooses the most relevant ones (based on previous knowledge of the region, availability, etc.) and runs a series of experiments combining the available data in order to find the combination that provides the best prediction. On the other hand, most climatic databases around the world present missing data, due to many reasons including sensor malfunction, transmission errors, and data handling errors. The aim of this is project is developing intelligent software tools, to propose solutions for the problem of selection of variables for the estimation of Solar Radiation and data imputation. The tools developed in the project should be able to:

  • Select Relevant Variables for Solar Radiation Estimation or Prediction
  • Allow efficient imputation of missing data.
These tools should address the problem of variable selection and improve the estimations and predictions by allowing more efficient use of the data available. The methodologies developed should work on solar radiation and other climatic variables but are validated on real data from Tucumán, Argentina, provided by the Agrometeorological section of the Estación Experimental Agroindustrial Obispo Colombres.


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