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Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks

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  • Wu, Zhou
  • Zeng, Shaoxiong
  • Jiang, Ruiqi
  • Zhang, Haoran
  • Yang, Zhile

Abstract

Wind power is one of the most promising renewable energy for its abundant resources, economically competitive, and environmentally friendly. Nevertheless, the wind power is challenging used in the power generation system due to its intermittency. Therefore, to improve the utilization ratio of wind power, the common method is adopting a prediction model for scheduling the generation industries. However, the information offered by single-step models hardly assists managers control their generators, and existing multi-step prediction models ignore the temporal dependence among predicted steps. Thus, a hybrid method based on a deep-chain echo state network (DCESN) and variational mode decomposition (VMD) is proposed to enhance the mapping capability for wind power multi-step prediction. The multiple reservoirs of deep-chain echo state network are concatenated as a chain structure, which could congregate the temporal relations among future steps shown in visualized graphs. Three comparative experiments demonstrate that the proposed hybrid method has promising performance on wind power multi-step prediction.

Suggested Citation

  • Wu, Zhou & Zeng, Shaoxiong & Jiang, Ruiqi & Zhang, Haoran & Yang, Zhile, 2023. "Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks," Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:energy:v:270:y:2023:i:c:s0360544223003006
    DOI: 10.1016/j.energy.2023.126906
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    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    3. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    4. Cui, Yang & Chen, Zhenghong & He, Yingjie & Xiong, Xiong & Li, Fen, 2023. "An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events," Energy, Elsevier, vol. 263(PC).
    5. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    6. Dai, Juchuan & Yang, Xin & Wen, Li, 2018. "Development of wind power industry in China: A comprehensive assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 156-164.
    7. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    8. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    9. Dhiman, Harsh S. & Deb, Dipankar & Guerrero, Josep M., 2019. "Hybrid machine intelligent SVR variants for wind forecasting and ramp events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 369-379.
    10. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
    11. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2021. "A novel genetic LSTM model for wind power forecast," Energy, Elsevier, vol. 223(C).
    12. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    13. Ye, Lin & Zhao, Yongning & Zeng, Cheng & Zhang, Cihang, 2017. "Short-term wind power prediction based on spatial model," Renewable Energy, Elsevier, vol. 101(C), pages 1067-1074.
    14. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    15. 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).
    16. Kong, Xiaobing & Ma, Lele & Wang, Ce & Guo, Shifan & Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Lee, Kwang Y., 2022. "Large-scale wind farm control using distributed economic model predictive scheme," Renewable Energy, Elsevier, vol. 181(C), pages 581-591.
    17. Alexey Mikhaylov & Nikita Moiseev & Kirill Aleshin & Thomas Burkhardt, 2020. "Global climate change and greenhouse effect," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(4), pages 2897-2913, June.
    18. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
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