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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. Zhang, Chu & Hua, Lei & Ji, Chunlei & Shahzad Nazir, Muhammad & Peng, Tian, 2022. "An evolutionary robust solar radiation prediction model based on WT-CEEMDAN and IASO-optimized outlier robust extreme learning machine," Applied Energy, Elsevier, vol. 322(C).
    2. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Lu, Haiyan & Zhang, Linyue, 2022. "A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory," Resources Policy, Elsevier, vol. 79(C).
    3. Li, Jingrui & Wang, Jiyang & Li, Zhiwu, 2023. "A novel combined forecasting system based on advanced optimization algorithm - A study on optimal interval prediction of wind speed," Energy, Elsevier, vol. 264(C).
    4. Zhang, Chu & Ma, Huixin & Hua, Lei & Sun, Wei & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction," Energy, Elsevier, vol. 254(PA).
    5. Wei, Danxiang & Wang, Jianzhou & Niu, Xinsong & Li, Zhiwu, 2021. "Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks," Applied Energy, Elsevier, vol. 292(C).
    6. Tian, Zhirui & Wang, Jiyang, 2022. "Variable frequency wind speed trend prediction system based on combined neural network and improved multi-objective optimization algorithm," Energy, Elsevier, vol. 254(PA).
    7. Wang, Jianzhou & An, Yining & Li, Zhiwu & Lu, Haiyan, 2022. "A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting," Energy, Elsevier, vol. 251(C).
    8. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
    9. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
    10. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    11. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
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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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