An advanced statistical method for wind power forecasting

Sideratos, G; Hatziargyriou, ND

dc.contributor.author	Sideratos, G	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T01:25:53Z
dc.date.available	2014-03-01T01:25:53Z
dc.date.issued	2007	en
dc.identifier.issn	0885-8950	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17791
dc.subject	Fuzzy sets	en
dc.subject	Radial base function networks	en
dc.subject	Self- organized map	en
dc.subject	Wind power forecasting	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Electric power generation	en
dc.subject.other	Electric power measurement	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Self organizing maps	en
dc.subject.other	Weather forecasting	en
dc.subject.other	Operational planning	en
dc.subject.other	Wind farm	en
dc.subject.other	Wind speed	en
dc.subject.other	Wind power	en
dc.title	An advanced statistical method for wind power forecasting	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TPWRS.2006.889078	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TPWRS.2006.889078	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	This paper presents an advanced statistical method for wind power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of wind speed and direction interpolated at the site of the wind farm. A self-organized map is trained to classify the forecasted local wind speed provided by the meteorological services. A unique feature of the method is that following a preliminary wind power prediction, it provides an estimation of the quality of the meteorological forecasts that is subsequently used to improve predictions. The proposed method is suitable for operational planning of power systems with increased wind power penetration, i.e., forecasting horizon of 48 h ahead and for wind farm operators trading in electricity markets. Application of the forecasting method on the power production of an actual wind farm shows the validity of the method. © 2007 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Power Systems	en
dc.identifier.doi	10.1109/TPWRS.2006.889078	en
dc.identifier.isi	ISI:000243914400029	en
dc.identifier.volume	22	en
dc.identifier.issue	1	en
dc.identifier.spage	258	en
dc.identifier.epage	265	en