@ARTICLE{will10,
    author = {Will, Adrian and Bustos, Jorge and Bocco, Mónica and Gotay, Jorge and Lamelas, Cesar},
     title = {On the use of Niching Genetic Algorithms for variable selection in Solar Radiation Estimation},
   journal = {Renewable Energy Journal},
      year = {2013},
  abstract = {Prediction of climatic variables, in particular those related to wind and solar
radiation, has developed a huge interest in recent years, mainly due to its
applications to renewable energy. In many cases there exist 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.
In this work we present two applications of Niching Genetic Algorithms
to solve the problem of selection of variables for the estimation of Solar Radiation.
On one hand, this methodology is able to estimate a given climatic
variable using databases with missing data, since the algorithm can compensate it by the use of others. On the other hand, we present a methodology  that allows us to select the relevant input variables for a given climatic variable estimation or prediction problem, in a systematic way, using the same Genetic Algorithm with different parameters.
Both methods were tested in the estimation of daily Global Solar Radiation
in El Colmenar (Tucum´an, Argentina), using linear regression on data
from 14 weather stations spread along the north of Argentina. The results
obtained show that the methodology is appropriate (RMSE = 2.32 and
R = 0.93 using only 52 out of 329 initial variables).}
}