Skip to main content

Advertisement

SpringerLink
Log in

A Self-Adaptive Approach for Particle Swarm Optimization Applied to Wind Speed Forecasting

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

Operational research has made meaningful contributions to practical forecasting in organizations. An area of substantial activity has been in nonlinear modeling. Based on Particle Swarm Optimization, we discuss a nonlinear method where a self-adaptive approach, named as Particle Swarm Optimization with aging and weakening factors, was applied to training a Focused Time Delay Neural Network. Three freely available benchmark datasets were used to demonstrate the features of the proposed approach compared to the Backpropagation algorithm, Differential Evolution and the Particle Swarm Optimization method when applied for training the artificial neural network. Even acknowledging that the effort in comparing methods across multiple empirical datasets is certainly substantial, the proposed algorithm was used to produce 30 min, 1, 3 and 6 h ahead predictions of wind speed at one site in Brazil. The use of the proposed algorithm goes further than only training the artificial neural network, but also searching the best number of hidden neurons and number of lags. The results have shown that the modified Particle Swarm Optimization algorithm obtained better results in all predictions horizons, and the use of it has remarkably reduced the training time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Arce, F., Zamora, E., Sossa, H., & Barrn, R. (2016). Dendrite morphological neural networks trained by differential evolution. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. doi:10.1109/SSCI.2016.7850259.

  • Beheshti, Z., Shamsuddin, S. M., & Sulaiman ,S. (2014). Fusion global-local-topology particle swarm optimization for global optimization problems. Mathematical Problems in Engineering 2014.

  • Box, G. E. & Jenkins, G. M. (1970). Time series analysis forecasting and control. http://opac.inria.fr/record=b1108766.

  • Chang, L. C., Chen, P. A., & Chang, F. J. (2012). Reinforced two-step-ahead weight adjustment technique for online training of recurrent neural networks. IEEE Transactions on Neural Networks and Learning Systems, 23(8), 1269–1278.

    Article  Google Scholar 

  • Chen, W. N., Zhang, J., Lin, Y., Chen, N., Zhan, Z. H., Chung, H. S. H., et al. (2013). Particle swarm optimization with an aging leader and challengers. IEEE Transactions on Evolutionary Computation, 17(2), 241–258.

    Article  Google Scholar 

  • Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 4–31. doi:10.1109/TEVC.2010.2059031.

    Article  Google Scholar 

  • Elloumi, W., Baklouti, N., Abraham, A., & Alimi, A. M. (2014). The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization. Journal of Intelligent & Fuzzy Systems, 27(1), 515–525.

    MATH  MathSciNet  Google Scholar 

  • Engelbrecht, A. P. (2007). Computational intelligence: An introduction. Hoboken: Wiley.

    Book  Google Scholar 

  • Garg, H. (2016). A hybrid pso-ga algorithm for constrained optimization problems. Applied Mathematics and Computation, 274, 292–305.

    Article  MathSciNet  Google Scholar 

  • Gavrilov, L. A., & Gavrilova, N. S. (2002). Evolutionary theories of aging and longevity. The Scientific World Journal, 2, 339–356.

    Article  Google Scholar 

  • Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M., & Draxl, C. (2011). The state-of-the-art in short-term prediction of wind power: A literature overview. Tech. rep., ANEMOS. Plus.

  • Glc, Åaban, & Kodaz, H. (2015). A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization. Engineering Applications of Artificial Intelligence, 45, 33–45.

    Article  Google Scholar 

  • Goldsmith, T. C. (2004). Aging as an evolved characteristic–Weismanns theory reconsidered. Medical hypotheses, 62(2), 304–308.

    Article  Google Scholar 

  • Goldsmith, T. (2006). The evolution of aging. Azinet LLC.

  • GWEC GWS (2017). Global wind energy council. http://www.gwec.net.

  • Huebner, U., Abraham, N., & Weiss, C. (1989). Dimensions and entropies of chaotic intensity pulsations in a single-mode far-infrared nh 3 laser. Physical Review A, 40(11), 6354.

    Article  Google Scholar 

  • Hyndman, R. (2017). Time series data library. https://datamarket.com/data/list/?q=provider:tsdl.

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, Perth, WA, Australia, pp. 1942–1948.

  • Kennedy, J., & Mendes, R. (2002). Population structure and particle swarm performance. In Evolutionary Computation, 2002. CEC’02. Proceedings of the 2002 Congress on (Vol. 2, pp. 1671–1676). doi:10.1109/CEC.2002.1004493.

  • Lim, W. H., & Isa, N. A. M. (2014). Particle swarm optimization with increasing topology connectivity. Engineering Applications of Artificial Intelligence, 27, 80–102.

    Article  Google Scholar 

  • Liu, H., Tian, H., Liang, X. F., & Li, Y. F. (2015). Wind speed forecasting approach using secondary decomposition algorithm and elman neural networks. Applied Energy, 157, 183–194.

    Article  Google Scholar 

  • Liu, R., Li, J., Fan, J., Mu, C., & Jiao, L. (2017). A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization. European Journal of Operational Research, 261(3), 1028–1051.

    Article  MathSciNet  Google Scholar 

  • Mackey, M. C., & Glass, L. (1977). Oscillation and chaos in physiological control systems. Science, 197(4300), 287–289.

    Article  Google Scholar 

  • Mao, Y., & Shaoshuai, W. (2016). A review of wind power forecasting prediction. In 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp. 1–7. doi:10.1109/PMAPS.2016.7764085.

  • Mao, M., Ling, J., Chang, L., Hatziargyriou, N. D., Zhang, J., & Ding, Y. (2016). A novel short-term wind speed prediction based on mfec. IEEE Journal of Emerging and Selected Topics in Power Electronics, 4(4), 1206–1216. doi:10.1109/JESTPE.2016.2590834.

    Article  Google Scholar 

  • Mao, B., Xie, Z., Wang, Y., Handroos, H., Wu, H., & Shi, S. (2017). A hybrid differential evolution and particle swarm optimization algorithm for numerical kinematics solution of remote maintenance manipulators. Fusion Engineering and Design.

  • Raza, S., Mokhlis, H., Arof, H., Naidu, K., Laghari, J. A., & Khairuddin, A. S. M. (2016). Minimum-features-based ann-pso approach for islanding detection in distribution system. IET Renewable Power Generation, 10(9), 1255–1263. doi:10.1049/iet-rpg.2016.0080.

    Article  Google Scholar 

  • Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In The 1998 IEEE International Conference on Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence (pp. 69–73). IEEE.

  • Shi, Y., & Eberhart, R. C. (1999). Empirical study of particle swarm optimization. In Proceedings of the 1999 Congress on Evolutionary Computation, 1999. CEC 99, Vol. 3. IEEE.

  • Ssekulima, E. B., Anwar, M. B., Hinai, A. A., & Moursi, M. S. E. (2016). Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: A review. IET Renewable Power Generation, 10(7), 885–989.

    Article  Google Scholar 

  • Storn, R., & Price, K. (1997). Differential evolution–A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.

  • Taylor, K. E. (2000). Summarizing multiple aspects of model performance in a single diagram. Program for Climate Model Diagnosis and Intercomparison: Lawrence Livermore National Laboratory, University of California.

    Google Scholar 

  • Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley Series in Behavioral Science: Quantitative Methods, 1(68), 2–70.

    MATH  Google Scholar 

  • Van den Bergh, F., & Engelbrecht, A. P. (2004). A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 225–239.

    Article  Google Scholar 

  • Weismann, A. (1882). Ueber die Dauer des Lebens. G. Fischer.

  • Williams, D. R. G. H. R., & Hinton, G. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.

    Article  MATH  Google Scholar 

  • Yeh, W. C. (2013). New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series. IEEE Transactions on Neural Networks and Learning Systems, 24(4), 661–665.

    Article  Google Scholar 

  • Zhang, Y., Wang, S., & Ji, G. (2015). A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering 2015.

Download references

Acknowledgements

The authors would like to thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), for its support.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Universidade Federal do Ceará, Campus do Pici, Caixa Postal 6001, Fortaleza, Ceará, 60455-760, Brazil

    E. C. Bezerra, R. P. S. Leão & A. P. de S. Braga

Authors
  1. E. C. Bezerra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. P. S. Leão
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. P. de S. Braga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. C. Bezerra.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bezerra, E.C., Leão, R.P.S. & Braga, A.P.d.S. A Self-Adaptive Approach for Particle Swarm Optimization Applied to Wind Speed Forecasting. J Control Autom Electr Syst 28, 785–795 (2017). https://doi.org/10.1007/s40313-017-0339-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-017-0339-6

Keywords

Search

Navigation