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A wind speed prediction system based on new data preprocessing strategy and improved multi-objective optimizer

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  • Tian, Zhirui
  • Wang, Jiyang

Abstract

The way to predict wind speed more accurately has always been one of the most important problems. The current combination prediction models have some limitations in data preprocessing, optimization algorithms and so on, which decreased the accuracy of the model. In order to solve the above problems, a new wind speed prediction system is proposed including a new data preprocessing strategy, combined neural network prediction and an improved multi-objective optimizer based on Halton low-discrepancy sequence and multi-distributed disturbance operator. The main contributions include: Firstly, a new data preprocessing strategy (GMM-ICEE) is proposed, which not only retains the characteristics of the wind speed data, but also removes the noise well. Secondly, Halton low-discrepancy sequence is introduced in the population initialization, which can effectively escape from local optimal solution. Thirdly, a new disturbance operator is proposed, which effectively solve the problem of optimal stagnation. Through six groups of experiments and two groups of discussions, it is verified that the accuracy, stability, generalization ability and advanced nature of the wind speed prediction system are satisfactory. Compared with traditional methods, the proposed wind speed prediction system improved by about 10% in prediction accuracy (MAPE) and by about 5% in CPU operation time.

Suggested Citation

  • Tian, Zhirui & Wang, Jiyang, 2023. "A wind speed prediction system based on new data preprocessing strategy and improved multi-objective optimizer," Renewable Energy, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008388
    DOI: 10.1016/j.renene.2023.118932
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    References listed on IDEAS

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    1. Zhang, Ziyuan & Wang, Jianzhou & Wei, Danxiang & Luo, Tianrui & Xia, Yurui, 2023. "A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network," Renewable Energy, Elsevier, vol. 204(C), pages 11-23.
    2. Sun, Xifu & Croke, Barry & Roberts, Stephen & Jakeman, Anthony, 2021. "Comparing methods of randomizing Sobol′ sequences for improving uncertainty of metrics in variance-based global sensitivity estimation," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. Hofer, Roswitha, 2018. "Halton-type sequences in rational bases in the ring of rational integers and in the ring of polynomials over a finite field," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 143(C), pages 78-88.
    4. Wang, Jianzhou & Wang, Shuai & Li, Zhiwu, 2021. "Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression," Renewable Energy, Elsevier, vol. 179(C), pages 1246-1261.
    5. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    6. Xiao, Yu-jie & Wang, Xiao-kang & Wang, Jian-qiang & Zhang, Hong-yu, 2021. "An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using ICEEMDAN-ICA," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    7. 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).
    8. Wang, Shuai & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2021. "A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches," Energy, Elsevier, vol. 234(C).
    9. Li, Jingrui & Wang, Jianzhou & Zhang, Haipeng & Li, Zhiwu, 2022. "An innovative combined model based on multi-objective optimization approach for forecasting short-term wind speed: A case study in China," Renewable Energy, Elsevier, vol. 201(P1), pages 766-779.
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    3. Lin, Shengmao & Wang, Shu & Xu, Xuefang & Li, Ruixiong & Shi, Peiming, 2024. "GAOformer: An adaptive spatiotemporal feature fusion transformer utilizing GAT and optimizable graph matrixes for offshore wind speed prediction," Energy, Elsevier, vol. 292(C).

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