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Ultra-Short-Term Wind Speed Forecasting Using the Hybrid Model of Subseries Reconstruction and Broad Learning System

Author

Listed:
  • Ming Pang

    (School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Lei Zhang

    (School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710048, China)

  • Yajun Zhang

    (School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Ao Zhou

    (School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Jianming Dou

    (School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Zhepeng Deng

    (School of Chemical and Chemical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

The traditional decomposition–combination wind speed forecasting model has high complexity and a long calculation time. As a result, an ultra-short-term wind speed hybrid forecasting model based on a broad learning system (BLS) that combines improved variational mode decomposition (EPSO-VMD, EVMD) and subseries reconstruction (SR) is proposed in this work. The values of K and α in the EVMD are determined by minimum mean envelope entropy (MMEE) and enhanced particle swarm optimization (EPSO), and EVMD is used to decompose the original wind speed data. SR is applied to recombine the subseries obtained by EVMD to improve the forecasting efficiency. The sample entropy (SE) is used to quantify the subseries’ complexity, and they are then adaptively divided into high-entropy and low-entropy subseries. Adjacent high-entropy subseries of approximate entropy values are merged to obtain a new group of reconstructed high-entropy subseries, while the low-entropy subseries merge into a new subseries as well. Then, the forecasting results of the reconstructed high- and low-entropy subseries are calculated via the BLS and ARIMA models. Numerical simulation results show that the proposed method is more effective than traditional methods.

Suggested Citation

  • Ming Pang & Lei Zhang & Yajun Zhang & Ao Zhou & Jianming Dou & Zhepeng Deng, 2022. "Ultra-Short-Term Wind Speed Forecasting Using the Hybrid Model of Subseries Reconstruction and Broad Learning System," Energies, MDPI, vol. 15(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4492-:d:843317
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    References listed on IDEAS

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