ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment

Quej, Victor H and Almorox, Javier and Arnaldo, Javier A. and Saito, Laurel (2017). ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. "Journal of Atmospheric And Solar-Terrestrial Physics", v. 155 ; pp. 62-70. ISSN 1364-6826. https://doi.org/10.1016/j.jastp.2017.02.002.

Description

Title: ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment
Author/s:
  • Quej, Victor H
  • Almorox, Javier
  • Arnaldo, Javier A.
  • Saito, Laurel
Item Type: Article
Título de Revista/Publicación: Journal of Atmospheric And Solar-Terrestrial Physics
Date: March 2017
ISSN: 1364-6826
Volume: 155
Subjects:
Faculty: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (905kB) | Preview

Abstract

Daily solar radiation is an important variable in many models. In this paper, the accuracy and performance of three soft computing techniques (i.e., adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM) were assessed for predicting daily horizontal global solar radiation from measured meteorological variables in the Yucatán Peninsula, México. Model performance was assessed with statistical indicators such as root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The performance assessment indicates that the SVM technique with requirements of daily maximum and minimum air temperature, extraterrestrial solar radiation and rainfall has better performance than the other techniques and may be a promising alternative to the usual approaches for predicting solar radiation.

More information

Item ID: 45030
DC Identifier: http://oa.upm.es/45030/
OAI Identifier: oai:oa.upm.es:45030
DOI: 10.1016/j.jastp.2017.02.002
Official URL: http://www.sciencedirect.com/science/article/pii/S1364682616302917
Deposited by: Memoria Investigacion
Deposited on: 13 Mar 2017 17:37
Last Modified: 31 Mar 2019 22:30
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM