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A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction

Author

Listed:
  • Longnv Huang

    (School of Information Engineering, Nanchang University, Nanchang 330031, China
    These authors contributed equally to this work.)

  • Qingyuan Wang

    (School of Information Engineering, Nanchang University, Nanchang 330031, China
    These authors contributed equally to this work.)

  • Jiehui Huang

    (School of Information Engineering, Nanchang University, Nanchang 330031, China
    These authors contributed equally to this work.)

  • Limin Chen

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Yin Liang

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Peter X. Liu

    (School of Information Engineering, Nanchang University, Nanchang 330031, China
    Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada)

  • Chunquan Li

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

Abstract

A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed prediction by combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the sample entropy (SE), optimized recurrent broad learning system (ORBLS), and broadened temporal convolutional network (BTCN). First, ICEEMDAN is introduced to smooth the nonlinear part of the wind speed data by decomposing the raw wind speed data into a series of sequences. Second, SE is applied to quantitatively assess the complexity of each sequence. All sequences are divided into simple sequence set and complex sequence set based on the values of SE. Third, based on the typical broad learning system (BLS), we propose ORBLS with cyclically connected enhancement nodes, which can better capture the dynamic characteristics of the wind. The improved particle swarm optimization (PSO) is used to optimize the hyper-parameters of ORBLS. Fourth, we propose BTCN by adding a dilated causal convolution layer in parallel to each residual block, which can effectively alleviate the local information loss of the temporal convolutional network (TCN) in case of insufficient time series data. Note that ORBLS and BTCN can effectively predict the simple and complex sequences, respectively. To validate the performance of the proposed model, we conducted three predictive experiments on four data sets. The experimental results show that our model obtains the best predictive results on all evaluation metrics, which fully demonstrates the accuracy and robustness of the proposed model.

Suggested Citation

  • Longnv Huang & Qingyuan Wang & Jiehui Huang & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction," Energies, MDPI, vol. 15(13), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4895-:d:855549
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    References listed on IDEAS

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