Training a FIS with EPSO under an Entropy Criterion for Wind Power prediction
Vladimiro Miranda, C. Cerqueira, Cláudio Monteiro · 2006
This paper summarizes efforts in understanding the possible application of information theoretic learning principles to power systems. It presents the application of Renyi's entropy combined with Parzen windows as a measure of information content of the error distribution in model parameter estimation in supervised learning. It illustrates the concept with an application to the prediction of power generated in a wind park, made by Takagi-Sugeno fuzzy inference systems, whose parameters are discovered with an EPSO-evolutionary particle swarm optimization algorithm