Training a FIS with EPSO under an entropy criterion for wind power prediction

• Type: Article in International Conference Proceedings Book
• Date: 2006

Authors

Vladimiro Miranda (Author) FEUP; Cristina Cerqueira (Author) Other; Cláudio Monteiro (Author) FEUP

Conference proceedings International

Title: 2006 International Conference on Probabilistic Methods Applied to Power Systems, Vols 1 and 2

Pages: 98-105

9th International Conference on Probabilistic Methods Applied to Power Systems

Stockholm, SWEDEN, JUN 11-15, 2006

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 10 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences > Engineering > Electrical engineering

Other information

Authenticus ID: P-004-R65

DOI: 10.1109/pmaps.2006.360208

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8