Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models

Fernando Antonanzas-Torres, Andres Sanz-Garcia, Javier Antonanzas-Torres, Oscar Perpiñán-Lamiguero, Francisco Javier Martínez-de-Pisón-Ascacibar

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Abstract

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.
Original languageEnglish
Title of host publicationSoft Computing Applications for Renewable Energy and Energy Efficiency
EditorsMaria del Socorro García Cascales, Juan Miguel Sánchez Lozano, Antonio David Masegosa Arredondo, Carlos Cruz Corona
Number of pages22
PublisherIGI Global
Publication date2015
Pages1-22
ISBN (Print)9781466666313
DOIs
Publication statusPublished - 2015
MoE publication typeB2 Book chapter

Fields of Science

  • 113 Computer and information sciences
  • 317 Pharmacy

Cite this

Antonanzas-Torres, F., Sanz-Garcia, A., Antonanzas-Torres, J., Perpiñán-Lamiguero, O., & Martínez-de-Pisón-Ascacibar, F. J. (2015). Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models. In M. D. S. G. Cascales, J. M. S. Lozano, A. D. M. Arredondo, & C. C. Corona (Eds.), Soft Computing Applications for Renewable Energy and Energy Efficiency (pp. 1-22). IGI Global. https://doi.org/10.4018/978-1-4666-6631-3
Antonanzas-Torres, Fernando ; Sanz-Garcia, Andres ; Antonanzas-Torres, Javier ; Perpiñán-Lamiguero, Oscar ; Martínez-de-Pisón-Ascacibar, Francisco Javier. / Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models. Soft Computing Applications for Renewable Energy and Energy Efficiency. editor / Maria del Socorro García Cascales ; Juan Miguel Sánchez Lozano ; Antonio David Masegosa Arredondo ; Carlos Cruz Corona. IGI Global, 2015. pp. 1-22
@inbook{d78efa43333846f3a1c5e9335b5f0ada,
title = "Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models",
abstract = "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.",
keywords = "113 Computer and information sciences, 317 Pharmacy",
author = "Fernando Antonanzas-Torres and Andres Sanz-Garcia and Javier Antonanzas-Torres and Oscar Perpi{\~n}{\'a}n-Lamiguero and Mart{\'i}nez-de-Pis{\'o}n-Ascacibar, {Francisco Javier}",
year = "2015",
doi = "10.4018/978-1-4666-6631-3",
language = "English",
isbn = "9781466666313",
pages = "1--22",
editor = "Cascales, {Maria del Socorro Garc{\'i}a} and Lozano, {Juan Miguel S{\'a}nchez} and Arredondo, {Antonio David Masegosa} and Corona, {Carlos Cruz}",
booktitle = "Soft Computing Applications for Renewable Energy and Energy Efficiency",
publisher = "IGI Global",
address = "United States",

}

Antonanzas-Torres, F, Sanz-Garcia, A, Antonanzas-Torres, J, Perpiñán-Lamiguero, O & Martínez-de-Pisón-Ascacibar, FJ 2015, Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models. in MDSG Cascales, JMS Lozano, ADM Arredondo & CC Corona (eds), Soft Computing Applications for Renewable Energy and Energy Efficiency. IGI Global, pp. 1-22. https://doi.org/10.4018/978-1-4666-6631-3

Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models. / Antonanzas-Torres, Fernando; Sanz-Garcia, Andres; Antonanzas-Torres, Javier; Perpiñán-Lamiguero, Oscar; Martínez-de-Pisón-Ascacibar, Francisco Javier.

Soft Computing Applications for Renewable Energy and Energy Efficiency. ed. / Maria del Socorro García Cascales; Juan Miguel Sánchez Lozano; Antonio David Masegosa Arredondo; Carlos Cruz Corona. IGI Global, 2015. p. 1-22.

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

TY - CHAP

T1 - Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models

AU - Antonanzas-Torres, Fernando

AU - Sanz-Garcia, Andres

AU - Antonanzas-Torres, Javier

AU - Perpiñán-Lamiguero, Oscar

AU - Martínez-de-Pisón-Ascacibar, Francisco Javier

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - 113 Computer and information sciences

KW - 317 Pharmacy

U2 - 10.4018/978-1-4666-6631-3

DO - 10.4018/978-1-4666-6631-3

M3 - Chapter

SN - 9781466666313

SP - 1

EP - 22

BT - Soft Computing Applications for Renewable Energy and Energy Efficiency

A2 - Cascales, Maria del Socorro García

A2 - Lozano, Juan Miguel Sánchez

A2 - Arredondo, Antonio David Masegosa

A2 - Corona, Carlos Cruz

PB - IGI Global

ER -

Antonanzas-Torres F, Sanz-Garcia A, Antonanzas-Torres J, Perpiñán-Lamiguero O, Martínez-de-Pisón-Ascacibar FJ. Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models. In Cascales MDSG, Lozano JMS, Arredondo ADM, Corona CC, editors, Soft Computing Applications for Renewable Energy and Energy Efficiency. IGI Global. 2015. p. 1-22 https://doi.org/10.4018/978-1-4666-6631-3