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 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 present project is aimed at developing intelligent software tools, in order 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 shouls address the problem of variable selection and improve the estimations and predictions by allowing a 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.
Missing Data Imputation in Solar Radiation
We are always interested in new challenges and looking for new motivated people to work with.
If you are interested in colaborated with us, please contact us.
GITIA was founded on 2008 in order to promote and apply recent research works on advanced technologies to different of domains.
Our main application areas cover collaborative work, decision-helping tools and distributed problem solving.