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

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  • Alma Y. Alanis
  • Luis J. Ricalde
  • Chiara Simetti
  • Francesca Odone

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.

Suggested Citation

  • Alma Y. Alanis & Luis J. Ricalde & Chiara Simetti & Francesca Odone, 2013. "Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, June.
    • Handle: RePEc:hin:jnlmpe:197690
    DOI: 10.1155/2013/197690
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    Cited by:

    1. Alma Y. Alanis & Oscar D. Sanchez & Jesus G. Alvarez, 2021. "Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks," Mathematics, MDPI, vol. 9(10), pages 1-18, May.

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