Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 197690, 9 pages
http://dx.doi.org/10.1155/2013/197690
Research Article

Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

1CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Colonia Las Aguilas, 45080 Zapopan, JAL, Mexico
2UADY, Faculty of Engineering, Avenida Industrias no Contaminantes por Periferico Norte, Apartado Postal 115 Cordemex, Merida, Yuc, Mexico
3DISI, Università degli Studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy

Received 29 March 2013; Accepted 27 May 2013

Academic Editor: Yudong Zhang

Copyright © 2013 Alma Y. Alanis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme.