Information Theoretic Learning applied to Wind Power Modeling

dc.contributor.author	Audun Botterud	en
dc.contributor.author	Vladimiro Miranda	en
dc.contributor.author	José Carlos Príncipe	en
dc.contributor.author	Jianhui Wang	en
dc.contributor.author	Ricardo Jorge Bessa	en
dc.date.accessioned	2017-11-16T12:48:09Z
dc.date.available	2017-11-16T12:48:09Z
dc.date.issued	2010	en
dc.description.abstract	This paper reports new results in adopting information theoretic learning concepts in the training of neural networks to perform wind power forecasts. The forecast 'goodness' is discussed under two paradigms: one is only concerned in measuring the deviation between the forecasted and realized values, the other is related with the value of the forecast in the electricity market for different agents. The results and conclusions are supported by a real case example.	en
dc.identifier.uri	http://repositorio.inesctec.pt/handle/123456789/1774
dc.language	eng	en
dc.relation	4891	en
dc.relation	4882	en
dc.relation	208	en
dc.rights	info:eu-repo/semantics/embargoedAccess	en
dc.title	Information Theoretic Learning applied to Wind Power Modeling	en
dc.type	conferenceObject	en
dc.type	Publication	en