Entropy and Correntropy against Minimum Square Error in Off-Line and On-Line 3-day ahead Wind Power Forecasting

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. Also, the merits of on-line training against off-line training are evaluated, as a consequence of concept drift in wind pattern behavior. The results are favorably compared with the traditional minimum square error (MSE) criterion. Real case examples for two distinct wind parks are presented.