DocumentCode
2487834
Title
Information theoretic learning applied to wind power modeling
Author
Bessa, Ricardo J. ; Miranda, V. ; Principe, Jose C. ; Botterud, A. ; Wang, J.
Author_Institution
INESC Porto - Inst. de Eng. de Sist. e Comput. do Porto, Porto, Portugal
fYear
2010
fDate
18-23 July 2010
Firstpage
1
Lastpage
8
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.
Keywords
information theory; power markets; wind power; electricity market; information theoretic learning; wind power modeling; Artificial neural networks; Entropy; Predictive models; Training; Wind forecasting; Wind power generation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location
Barcelona
ISSN
1098-7576
Print_ISBN
978-1-4244-6916-1
Type
conf
DOI
10.1109/IJCNN.2010.5596362
Filename
5596362
Link To Document