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A novel hybrid wind speed prediction framework based on multi-strategy improved optimizer and new data pre-processing system with feedback mechanism

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  • Tian, Zhirui
  • Gai, Mei

Abstract

As a kind of renewable energy, wind energy has great potential for development and has been paid attention to by governments all over the world. However, due to the high uncertainty of wind speed, how to accurately predict wind speed and make use of wind energy has been recognized as a difficult problem. In order to solve this problem, a new hybrid wind speed prediction framework is proposed, which is composed of two subsystems, data preprocessing system and high-accuracy prediction system. In the system 1, the feedback mechanism is creatively added to the singular spectrum analysis (SSA) to find out the optimal decomposition-recombination strategy through the accuracy feedback. In the system 2, the unconstrained weighting mechanism is realized through the combination of combined neural network and multi-objective optimization algorithm to maximize the prediction accuracy on the premise of ensuring the stability of prediction. Besides, an improved meta-heuristic optimization algorithm based on cross-perturbation strategy (CP-JAYA) and its multi-objective form (MO-CPJAYA) are applied on two systems respectively to further improve the prediction ability of the framework. In 5 groups of experiments, the accuracy, advancement, generalization and sensitivity of the model are tested and compared with 13 other models. The proposed prediction framework has the best performance in all four sets of data. In 3 groups of discussions, we verify the advanced nature of CP-JAYA and MO-CPJAYA respectively through 13 single-objective test functions (CEC) and 4 multi-objective test functions (ZDT), and the speed advantage of the framework by recording the CPU running time.

Suggested Citation

  • Tian, Zhirui & Gai, Mei, 2023. "A novel hybrid wind speed prediction framework based on multi-strategy improved optimizer and new data pre-processing system with feedback mechanism," Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:energy:v:281:y:2023:i:c:s0360544223016195
    DOI: 10.1016/j.energy.2023.128225
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    References listed on IDEAS

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    1. Tian, Zhirui & Liu, Weican & Jiang, Wenqian & Wu, Chenye, 2024. "CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability," Energy, Elsevier, vol. 293(C).

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