Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study

R. Yacef, M. Benghanem and A. Mellit

Renewable Energy, 2012, vol. 48, issue C, 146-154

Abstract: This paper presents a comparative study between Bayesian Neural Network (BNN), classical Neural Network (NN) and empirical models for estimating the daily global solar irradiation (DGSR). An experimental meteorological database from 1998 to 2002 at Al-Madinah (Saudi Arabia) has been used. Four input parameters have been employed: air temperature, relative humidity, sunshine duration and extraterrestrial irradiation. Automatic relevance determination (ARD) method has investigated in order to select the optimum input parameters of the NN. Results show that the BNN performs better that other NN structures and empirical models.

Keywords: Daily global solar irradiation; Empirical models; Neural network; Bayesian neural network; Prediction (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148112002777
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:48:y:2012:i:c:p:146-154

DOI: 10.1016/j.renene.2012.04.036

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().