An advanced radial base structure for Wind Power Forecasting

Sideratos, GN; Hatziargyriou, ND

dc.contributor.author	Sideratos, GN	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T01:57:05Z
dc.date.available	2014-03-01T01:57:05Z
dc.date.issued	2008	en
dc.identifier.issn	10783466	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28339
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-50249155952&partnerID=40&md5=20166aef0e32f8141a2693803a48e854	en
dc.subject	Economic scheduling	en
dc.subject	Radial base function networks	en
dc.subject	Self-organized map	en
dc.subject	Wind Power Forecasting	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Electric power systems	en
dc.subject.other	Electric utilities	en
dc.subject.other	Farms	en
dc.subject.other	Forecasting	en
dc.subject.other	Neural networks	en
dc.subject.other	Renewable energy resources	en
dc.subject.other	Speed	en
dc.subject.other	Statistical methods	en
dc.subject.other	Wind effects	en
dc.subject.other	Wind power	en
dc.subject.other	Artificial intelligence techniques	en
dc.subject.other	Base structure	en
dc.subject.other	Economic scheduling	en
dc.subject.other	Electricity markets	en
dc.subject.other	Long-term prediction	en
dc.subject.other	Operational planning	en
dc.subject.other	Power outputs	en
dc.subject.other	Power production	en
dc.subject.other	Power systems	en
dc.subject.other	Radial base function networks	en
dc.subject.other	Radial basis networks	en
dc.subject.other	Radial basis neural networks	en
dc.subject.other	Self-organized map	en
dc.subject.other	Self-Organized Maps	en
dc.subject.other	Wind farms	en
dc.subject.other	Wind Power Forecasting	en
dc.subject.other	Wind speed	en
dc.subject.other	Electric load forecasting	en
dc.title	An advanced radial base structure for Wind Power Forecasting	en
heal.type	journalArticle	en
heal.publicationDate	2008	en
heal.abstract	This article presents an advanced Wind Power Forecasting method based on applications of artificial intelligence techniques. The method belongs to the statistical methods. It requires as input past wind power measurements and meteorological forecasts of wind speed and direction interpolated in the site of the wind farm. A self-organized map is trained to classify the forecasted local wind speed provided by the meteorological services. For each class of wind speed, a radial basis neural network is used to predict the windfarm power output. Different radial basis networks are applied for short-term and for long-term prediction. The proposed model is suitable for operational planning of power systems with increased wind power penetration, i.e. 48 hours and for Wind farm operators trading in electricity markets. Application of the forecasting method on the power production of actual wind farms shows the validity of the method.	en
heal.journalName	International Journal of Power and Energy Systems	en
dc.identifier.volume	28	en
dc.identifier.issue	3	en
dc.identifier.spage	281	en
dc.identifier.epage	288	en