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Title: Information Theoretic Learning applied to Wind Power Modeling
Authors: Audun Botterud
Vladimiro Miranda
José Carlos Príncipe
Jianhui Wang
Ricardo Jorge Bessa
Issue Date: 2010
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.
metadata.dc.type: conferenceObject
Appears in Collections:CPES - Indexed Articles in Conferences

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