Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques

Zervas, PL; Sarimveis, H; Palyvos, JA; Markatos, NCG

dc.contributor.author	Zervas, PL	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Palyvos, JA	en
dc.contributor.author	Markatos, NCG	en
dc.date.accessioned	2014-03-01T01:29:03Z
dc.date.available	2014-03-01T01:29:03Z
dc.date.issued	2008	en
dc.identifier.issn	0960-1481	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19092
dc.subject	Global solar irradiance	en
dc.subject	Neural networks	en
dc.subject	Prediction model	en
dc.subject.classification	Energy & Fuels	en
dc.subject.other	Correlation methods	en
dc.subject.other	Database systems	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Global solar irradiance	en
dc.subject.other	Prediction model	en
dc.subject.other	Solar energy	en
dc.subject.other	Correlation methods	en
dc.subject.other	Database systems	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Solar energy	en
dc.subject.other	artificial neural network	en
dc.subject.other	climate prediction	en
dc.subject.other	database	en
dc.subject.other	Gaussian method	en
dc.subject.other	irradiance	en
dc.subject.other	meteorology	en
dc.subject.other	model validation	en
dc.subject.other	numerical model	en
dc.subject.other	solar radiation	en
dc.subject.other	weather	en
dc.title	Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.renene.2007.09.020	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.renene.2007.09.020	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	In this study, a prediction model of global solar irradiance distribution on horizontal surfaces has been developed. The methodology is based on neural-network techniques and has been applied to the meteorological database of NTUA, Zografou Campus, Athens (37 degrees 58'26 '' N, 23 degrees 47'16 '' E). The investigation of the correlation between weather conditions, duration of daylight and the representative peak value of a Gaussian-type function plays an essential role in the development of the model. The weather conditions are categorized into six different states, whereas the daylight duration is obtained by familiar equations. Thereafter, a correction methodology for the Gaussian-type function-which stands for all six different states-is applied. Finally, the reliability of the developed model is investigated through a suitable validation procedure. (C) 2007 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Renewable Energy	en
dc.identifier.doi	10.1016/j.renene.2007.09.020	en
dc.identifier.isi	ISI:000255992300008	en
dc.identifier.volume	33	en
dc.identifier.issue	8	en
dc.identifier.spage	1796	en
dc.identifier.epage	1803	en