|
Descripción
|
|
|---|---|
| Most of the research on estimating Solar Global Irradiation (SGI) is based on the development of parametric models. However, the use of methods based on the use of statistics and machine-learning theories can provide a significant improvement in reducing the prediction errors. The chapter evaluates the performance of different Soft Computing (SC) methods, such as support vector regression and artificial neural networks-multilayer perceptron, in SGI modeling against classical parametric and lineal models. SC methods demonstrate a higher generalization capacity applied to SGI modeling than classic parametric models. As a result, SC models suppose an alternative to satellite-derived models to estimate SGI in near-to-present time in areas in which no pyranometers are installed nearby. | |
|
Internacional
|
Si |
|
DOI
|
10.4018/978-1-4666-6631-3.ch001 |
|
Edición del Libro
|
|
|
Editorial del Libro
|
IGI Global |
|
ISBN
|
978-146666632-0 |
|
Serie
|
|
|
Título del Libro
|
Soft Computing Applications for Renewable Energy and Energy Efficiency |
|
Desde página
|
1 |
|
Hasta página
|
22 |