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Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids
Joint Authors
Ricalde, Luis J.
Simetti, Chiara
Odone, Francesca
Alanis, Alma Y.
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Alanis, Alma Y.& Ricalde, Luis J.& Simetti, Chiara& Odone, Francesca. 2013. Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1031732
Modern Language Association (MLA)
Alanis, Alma Y.…[et al.]. Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids. Mathematical Problems in Engineering No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1031732
American Medical Association (AMA)
Alanis, Alma Y.& Ricalde, Luis J.& Simetti, Chiara& Odone, Francesca. Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1031732
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1031732