Title :
Improvement in wind power forecasting based on information entropy-related concepts
Author :
Bessa, Ricardo ; Miranda, Vladimiro ; Gama, Joao
Author_Institution :
Inst. de Eng. de Sist. e Comput. do Porto, INESC Porto, Porto
Abstract :
This paper reports new results in adopting entropy concepts to the training of mappers such as neural networks to perform wind power prediction as a function of wind characteristics (mainly speed and direction) in wind parks connected to a power grid. It also addresses the differences relevant to power system operation between off-line and on-line training of neural networks. Real case examples are presented.
Keywords :
entropy; learning (artificial intelligence); load forecasting; neural nets; power grids; power system analysis computing; wind power plants; information entropy; mapper training; neural networks; power grid; power system operation; wind parks; wind power forecasting; Entropy; Frequency; Neural networks; Power generation; Power generation economics; Wind energy; Wind energy generation; Wind forecasting; Wind power generation; Wind speed;
Conference_Titel :
Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE
Conference_Location :
Pittsburgh, PA
Print_ISBN :
978-1-4244-1905-0
Electronic_ISBN :
1932-5517
DOI :
10.1109/PES.2008.4596932
