Title :
Entropy and Correntropy Against Minimum Square Error in Offline and Online Three-Day Ahead Wind Power Forecasting
Author :
Bessa, Ricardo J. ; Miranda, Vladimiro ; Gama, João
Author_Institution :
INESC Porto, Inst. de Eng. de Sist. e Comput. do Porto, Porto, Portugal
Abstract :
This paper reports new results in adopting entropy concepts to the training of neural networks to perform wind power prediction as a function of wind characteristics (speed and direction) in wind parks connected to a power grid. Renyi´s entropy is combined with a Parzen windows estimation of the error pdf to form the basis of two criteria (minimum entropy and maximum correntropy) under which neural networks are trained. The results are favorably compared in online and offline training with the traditional minimum square error (MSE) criterion. Real case examples for two distinct wind parks are presented.
Keywords :
entropy; learning (artificial intelligence); least mean squares methods; load forecasting; power engineering computing; power grids; wind power plants; Parzen window estimation; Renyis entropy; correntropy; minimum square error method; neural network training; offline wind power prediction; online wind power forecasting; power grid; wind characteristics; wind parks; Correntropy; Parzen windows; entropy; neural networks; wind power forecasting;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2009.2030291
