Marco general para la extracción de información y estimación de radiación solar diaria
DOI:
https://doi.org/10.22517/23447214.24744Keywords:
Machine learning, Solar Energy, Principal Component AnalysisAbstract
This article presents a comparative study resulting from the design and simulation of a system of prediction of climatic conditions using machine learning models, in which the results obtained by using a database of environmental conditions are compared with another database generated from the treatment of the data through the analysis by principal components. In the first phase of the study, metadata is generated through the subspaces created with the analysis by principal components, a second phase consists of developing a system of prediction of climatic conditions using several machine learning models, which will use as a resource the original data and metadata generated in the first phase of the study , in the final phase of the study, both results are compared with the aim of observing the behavior of solar radiation inference systems. The proposed data processing strategy allows to extract information from environmental databases facilitating interpretation and observation as a time series of data, additionally, it is possible to build an experimental frame of reference for the inference of solar radiation using different supervised learning techniques on the databases generated.
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