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Research and Application of Hybrid Wind-Energy Forecasting Models Based on Cuckoo Search Optimization

Author

Listed:
  • Ru Hou

    (School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China)

  • Yi Yang

    (School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China)

  • Qingcong Yuan

    (Department of Statistics, Miami University, 304 B Upham Hall, Oxford, OH 45056, USA)

  • Yanhua Chen

    (School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China)

Abstract

Wind energy is crucial renewable and sustainable resource, which plays a major role in the energy mix in many countries around the world. Accurately forecasting the wind energy is not only important but also challenging in order to schedule the wind power generation and to ensure the security of wind-power integration. In this paper, four kinds of hybrid models based on cyclic exponential adjustment, adaptive coefficient methods and the cuckoo search algorithm are proposed to forecast the wind speed on large-scale wind farms in China. To verify the developed hybrid models’ effectiveness, wind-speed data from four sites of Xinjiang Uygur Autonomous Region located in northwest China are collected and analyzed. Multiple criteria are used to quantitatively evaluate the forecasting results. Simulation results indicate that (1) the proposed four hybrid models achieve desirable forecasting accuracy and outperform traditional back-propagating neural network, autoregressive integrated moving average as well as single adaptive coefficient methods, and (2) the parameters of hybrid models optimized by artificial intelligence contribute to higher forecasting accuracy compared with predetermined parameters.

Suggested Citation

  • Ru Hou & Yi Yang & Qingcong Yuan & Yanhua Chen, 2019. "Research and Application of Hybrid Wind-Energy Forecasting Models Based on Cuckoo Search Optimization," Energies, MDPI, vol. 12(19), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3675-:d:270787
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    References listed on IDEAS

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