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A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

Author

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  • Jujie Wang

    (Climate and Weather Disasters Collaborative Innovation Center, School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yanfeng Wang

    (Gansu Weather Modification Office, Lanzhou 730020, China)

  • Yaning Li

    (College of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

With the growing penetration of wind power into electric grids, improving wind speed prediction accuracy has become particularly valuable for the exploitation of wind power. In this paper, a novel hybrid strategy based on a three-phase signal decomposition (TPSD) technique, feature extraction (FE) and weighted regularized extreme learning machine (WRELM) is developed for multi-step ahead wind speed prediction. The TPSD including seasonal separation algorithm (SSA), fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) is proposed for the first time to handle the complex and irregular natures of wind speed comprehensively. The FE process is used to capture the useful features of wind speed fluctuations and determine the optimal inputs for a prediction model. The WRELM is employed as a basic predictor for building the prediction model by these selected features. Four real wind speed prediction cases are utilized to evaluate the proposed model, and experimental results verify the effectiveness of the proposed model compared with the benchmark models.

Suggested Citation

  • Jujie Wang & Yanfeng Wang & Yaning Li, 2018. "A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction," Energies, MDPI, vol. 11(2), pages 1-33, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:321-:d:129905
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    References listed on IDEAS

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