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Chaotic wind power time series prediction via switching data-driven modes

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  • Ouyang, Tinghui
  • Huang, Heming
  • He, Yusen
  • Tang, Zhenhao

Abstract

To schedule wind power efficiently and to mitigate the adverse effects caused by wind's intermittency and variability, an advanced wind power prediction model is proposed in this paper. This model is a combined model via switching different data-driven chaotic time series models. First, inputs of this model come from the reconstructed data based on the chaotic characteristics of wind power time series. Second, three different data mining algorithms are used to construct wind power prediction models individually. To obtain a regime for switching optimal models, a Markov chain is trained. Then, weights of different data-driven modes are calculated by the Markov chain switching regime, and used in the final combined model for wind power prediction. The industrial data from actual wind farms is studied. Results of the proposed model are compared with that of non-reconstructed input data, traditional data-driven models and two typical combined models. These results validate the superiority of proposed model on improving wind power prediction accuracy.

Suggested Citation

  • Ouyang, Tinghui & Huang, Heming & He, Yusen & Tang, Zhenhao, 2020. "Chaotic wind power time series prediction via switching data-driven modes," Renewable Energy, Elsevier, vol. 145(C), pages 270-281.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:270-281
    DOI: 10.1016/j.renene.2019.06.047
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    Citations

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    Cited by:

    1. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt & Tomasz Gulczyński, 2022. "Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms," Energies, MDPI, vol. 15(4), pages 1-30, February.
    2. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    3. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    4. Mohamed CHIKHI & Claude DIEBOLT, 2022. "Testing the weak form efficiency of the French ETF market with the LSTAR-ANLSTGARCH approach using a semiparametric estimation," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 228-253, June.
    5. Aneta Bełdycka-Bórawska & Piotr Bórawski & Michał Borychowski & Rafał Wyszomierski & Marek Bartłomiej Bórawski & Tomasz Rokicki & Luiza Ochnio & Krzysztof Jankowski & Bartosz Mickiewicz & James W. Dun, 2021. "Development of Solid Biomass Production in Poland, Especially Pellet, in the Context of the World’s and the European Union’s Climate and Energy Policies," Energies, MDPI, vol. 14(12), pages 1-22, June.
    6. Bingchun Liu & Shijie Zhao & Xiaogang Yu & Lei Zhang & Qingshan Wang, 2020. "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model," Energies, MDPI, vol. 13(18), pages 1-17, September.
    7. Ai, Chunyu & He, Shan & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power prediction method based on OVMD-PE and improved multi-objective state transition algorithm," Energy, Elsevier, vol. 278(C).
    8. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training," Applied Energy, Elsevier, vol. 355(C).
    9. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    10. Jin, Huaiping & Shi, Lixian & Chen, Xiangguang & Qian, Bin & Yang, Biao & Jin, Huaikang, 2021. "Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models," Renewable Energy, Elsevier, vol. 174(C), pages 1-18.
    11. Wang, Yun & Xu, Houhua & Song, Mengmeng & Zhang, Fan & Li, Yifen & Zhou, Shengchao & Zhang, Lingjun, 2023. "A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting," Applied Energy, Elsevier, vol. 333(C).
    12. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2023. "Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework," Energy, Elsevier, vol. 278(PA).
    13. Tang, Yugui & Yang, Kuo & Zheng, Yichu & Ma, Li & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training," Renewable Energy, Elsevier, vol. 224(C).
    14. Sun, Shaolong & Du, Zongjuan & Jin, Kun & Li, Hongtao & Wang, Shouyang, 2023. "Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy," Applied Energy, Elsevier, vol. 350(C).
    15. Paweł Piotrowski & Marcin Kopyt & Dariusz Baczyński & Sylwester Robak & Tomasz Gulczyński, 2021. "Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine," Energies, MDPI, vol. 14(5), pages 1-25, February.
    16. Wang, Huaqing & Tan, Zhongfu & Liang, Yan & Li, Fanqi & Zhang, Zheyu & Ju, Liwei, 2024. "A novel multi-layer stacking ensemble wind power prediction model under Tensorflow deep learning framework considering feature enhancement and data hierarchy processing," Energy, Elsevier, vol. 286(C).
    17. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.

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