Title :
Better prediction models for renewables by training with entropy concepts
Author :
Miranda, V. ; Cerqueira, C. ; Monteiro, C.
Author_Institution :
INESC Porto
Abstract :
Prediction models for generation from renewables are needed in the context of a power system with a diversified portfolio. The presentation discusses a new criterion and procedure to develop prediction models based on Renyi´s entropy combined with Parzen windows (an approach named information theoretic learning) that is applied to wind prediction and suggested as a better training paradigm for fuzzy or neural systems
Keywords :
entropy; neural nets; power engineering computing; renewable energy sources; wind power plants; Parzen windows; Renyi entropy; diversified portfolio; entropy concepts; fuzzy systems; information theoretic learning; neural systems; prediction models; training paradigm; wind prediction; Context modeling; Entropy; Kernel; Portfolios; Power generation; Power system modeling; Predictive models; Wind energy; Wind energy generation; Wind forecasting; Entropy; forecasting; prediction; wind power;
Conference_Titel :
Power Engineering Society General Meeting, 2006. IEEE
Conference_Location :
Montreal, Que.
Print_ISBN :
1-4244-0493-2
DOI :
10.1109/PES.2006.1709505
