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Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method

Author

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  • Zhao, Jing
  • Wang, Jianzhou
  • Guo, Zhenhai
  • Guo, Yanling
  • Lin, Wantao
  • Lin, Yihua

Abstract

At present, a single-valued deterministic simulation method is preferred choice for numerical wind speed forecasts. However, it remains difficult to meet the actual needs of both wind farms and grid systems, mainly owing to unavoidable uncertainties. The development of skilled numerical forecasting methods has become a critical issue and major challenge, and new capabilities and strategies for mitigating uncertainties in wind data derived from numerical models are highly sought after. On this topic, our study develops an improved ensemble method for day-ahead forecast of local wind speeds. The proposed method constructs an optimized system based on ensemble simulations of weather research and forecasting model, a Markov stochastic process, and an improved induced ordered weighted average approach that combines gray relationships with an evolutionary algorithm. The original contributions are concluded as: (i) using a Markov stochastic process, the observed information can be transferred to the ensemble system, which contributes to the accuracy improvement; and (ii) the optimized induced ordered weighted average model, with a member selection process, is a new data-driven ensemble method for numerical wind speed forecasting. Simulation indicates that the proposed method effectively reduces the uncertainties of numerical simulations, and performs better than other models. The simulation also shows that an ensemble with fewer members may generate better results than using a combination of all single members. This study is of great significance for both theoretical research and real applications for numerical wind speed forecasts at local sites.

Suggested Citation

  • Zhao, Jing & Wang, Jianzhou & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Lin, Yihua, 2019. "Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s030626191931520x
    DOI: 10.1016/j.apenergy.2019.113833
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    Citations

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    Cited by:

    1. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    2. Liu, Hui & Yang, Rui & Wang, Tiantian & Zhang, Lei, 2021. "A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections," Renewable Energy, Elsevier, vol. 165(P1), pages 573-594.
    3. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    4. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
    5. Zhang, Wenyu & Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong, 2020. "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting," Applied Energy, Elsevier, vol. 277(C).
    6. Zhao, Jing & Guo, Yiyi & Lin, Yihua & Zhao, Zhiyuan & Guo, Zhenhai, 2024. "A novel dynamic ensemble of numerical weather prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling," Energy, Elsevier, vol. 302(C).
    7. Nie, Ying & Liang, Ni & Wang, Jianzhou, 2021. "Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme," Applied Energy, Elsevier, vol. 301(C).
    8. Wang, Yi & Von Krannichfeldt, Leandro & Zufferey, Thierry & Toubeau, Jean-François, 2021. "Short-term nodal voltage forecasting for power distribution grids: An ensemble learning approach," Applied Energy, Elsevier, vol. 304(C).
    9. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
    10. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    11. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    12. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Li, Wenzhe & Li, Fei & Lee, Jay, 2021. "A unified Bayesian filtering framework for multi-horizon wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 178(C), pages 709-719.
    13. Liu, Hui & Duan, Zhu, 2020. "A vanishing moment ensemble model for wind speed multi-step prediction with multi-objective base model selection," Applied Energy, Elsevier, vol. 261(C).
    14. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    15. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    16. Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.

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