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Comparison of Kernel Functions in the Classification of Irradiance Zones from Multispectral Satellite Images
- Source :
- Revista Facultad de Ingeniería, Vol 30, Iss 58, Pp e13845-e13845 (2021)
- Publication Year :
- 2021
- Publisher :
- Universidad Pedagógica y Tecnológica de Colombia, 2021.
-
Abstract
- Due to the growing energy demand and the eminent global warming, there is special interest in the prediction of irradiance based on the reflectance obtained from satellites such as NASA Landsat, since it allows to know where it is more efficient to place photovoltaic receivers. Although there are studies for obtaining regression models with alternative Kernel functions, their performance for classification models is unknown and it is here where this research focuses. The study couples alternative Kernel functions to the support vector machines (SVM) algorithm for classification problems, where the best configuration for these algorithms is explored to finally obtain a set of irradiance maps zoned by class.
Details
- Language :
- English, Spanish; Castilian
- ISSN :
- 01211129 and 23575328
- Volume :
- 30
- Issue :
- 58
- Database :
- Directory of Open Access Journals
- Journal :
- Revista Facultad de Ingeniería
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f95fad9db967428a902fb69c93cbdf60
- Document Type :
- article
- Full Text :
- https://doi.org/10.19053/01211129.v30.n58.2021.13845