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

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.

[1]  George Stavrakakis,et al.  Wind power forecasting using advanced neural networks models , 1996 .

[2]  E.F. El-Saadany,et al.  One Day Ahead Prediction of Wind Speed and Direction , 2008, IEEE Transactions on Energy Conversion.

[3]  Yuang-Shung Lee,et al.  A Merged Fuzzy Neural Network and Its Applications in Battery State-of-Charge Estimation , 2007, IEEE Transactions on Energy Conversion.

[4]  Yuan-Kang Wu,et al.  A literature review of wind forecasting technology in the world , 2007, 2007 IEEE Lausanne Power Tech.

[5]  Edgar N. Sánchez,et al.  Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).

[6]  T. Funabashi,et al.  Application of Recurrent Neural Network to Long-Term-Ahead Generating Power Forecasting for Wind Power Generator , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[7]  Ganesh K. Venayagamoorthy,et al.  Online Training of a Generalized Neuron with Particle Swarm Optimization , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[8]  Jun Zeng,et al.  Control Technologies in Distributed Generation System Based on Renewable Energy , 2010 .

[9]  C. Mohan,et al.  Multi-phase generalization of the particle swarm optimization algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[10]  Ding Ming,et al.  Testbed for microgrid with multi-energy Generators , 2008, 2008 Canadian Conference on Electrical and Computer Engineering.

[11]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[12]  Chin-Teng Lin,et al.  A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[14]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[15]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[16]  M. Nørgaard,et al.  Advances in Derivative-Free State Estimation for Nonlinear Systems , 1998 .

[17]  Andrew Chi-Sing Leung,et al.  Dual extended Kalman filtering in recurrent neural networks , 2003, Neural Networks.

[18]  Edgar N. Sánchez,et al.  High Order Neural Networks for wind speed time series prediction , 2009, 2009 International Joint Conference on Neural Networks.

[19]  L. Cao.  Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[20]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[21]  Aleksandar Lazinica.  Particle Swarm Optimization , 2009 .

[22]  Alexander G. Loukianov,et al.  Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks , 2007, IEEE Transactions on Neural Networks.

[23]  Danil V. Prokhorov,et al.  Simple and conditioned adaptive behavior from Kalman filter trained recurrent networks , 2003, Neural Networks.

[24]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[25]  Alma Y. Alanis,et al.  A wind speed neural model with particle swarm optimization Kalman learning , 2012, World Automation Congress 2012.