EconPapers    
Economics at your fingertips  
 

Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia

Mohamed Benghanem and Adel Mellit

Energy, 2010, vol. 35, issue 9, 3751-3762

Abstract: In this paper, Radial Basis Function network (RBF) is used for modelling and predicting the daily global solar radiation data using other meteorological data such as air temperature, sunshine duration, and relative humidity. These data were recorded in the period 1998–2002 at Al-Madinah (Saudi Arabia) by the National Renewable Energy Laboratory. Four RBF-models have been developed for predicting the daily global solar radiation. It was found that the RBF-model which uses the sunshine duration and air temperature as input parameters, gives accurate results as the correlation coefficient in this case is 98.80%. A comparative study between developed RBF, Multilayer perceptron and conventional regression models are presented and discussed in this paper, In addition, an application for estimating the sizing of a stand-alone PV system at Al-Maidinah is presented in order to show the effectiveness of the developed RBF-model.

Keywords: Solar radiation; Prediction; Sizing; Photovoltaic; RBF network (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (27) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054421000294X
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:energy:v:35:y:2010:i:9:p:3751-3762

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Series data maintained by Dana Niculescu ().

 
Page updated 2017-09-29
Handle: RePEc:eee:energy:v:35:y:2010:i:9:p:3751-3762