Improved wind power forecasting using a combined neuro-fuzzy and artificial neural network model

Katsigiannis, YA; Tsikalakis, AG; Georgilakis, PS; Hatziargyriou, ND

dc.contributor.author	Katsigiannis, YA	en
dc.contributor.author	Tsikalakis, AG	en
dc.contributor.author	Georgilakis, PS	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T02:44:04Z
dc.date.available	2014-03-01T02:44:04Z
dc.date.issued	2006	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31653
dc.subject	Artificial neural networks	en
dc.subject	Prediction error	en
dc.subject	Wind power forecasting	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Electric load forecasting	en
dc.subject.other	Electric power systems	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Mathematical models	en
dc.subject.other	Statistical methods	en
dc.subject.other	Prediction error	en
dc.subject.other	Wind power forecasting	en
dc.subject.other	Wind power forecasting tool	en
dc.subject.other	Neural networks	en
dc.title	Improved wind power forecasting using a combined neuro-fuzzy and artificial neural network model	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11752912_13	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11752912_13	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	The intermittent nature of the wind creates significant uncertainty in the operation of power systems with increased wind power penetration, Considerable efforts have been made for the accurate prediction of the wind power using either statistical or physical models. In this paper, a method based on Artificial Neural Network (ANN) is proposed in order to improve the predictions of an existing neuro-fuzzy wind power forecasting model taking into account the evaluation results from the use of this wind power forecasting tool, Thus, an improved wind power forecasting is achieved and a better estimation of the confidence interval of the proposed model is provided. © Springer-Verlag Berlin Heidelberg 2006.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11752912_13	en
dc.identifier.isi	ISI:000238053100011	en
dc.identifier.volume	3955 LNAI	en
dc.identifier.spage	105	en
dc.identifier.epage	115	en