Please use this identifier to cite or link to this item:
http://repositorio.inesctec.pt/handle/123456789/1774
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. |
URI: | http://repositorio.inesctec.pt/handle/123456789/1774 |
metadata.dc.type: | conferenceObject Publication |
Appears in Collections: | CPES - Articles in International Conferences |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
PS-06333.pdf Restricted Access | 360.95 kB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.