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Deterministic and probabilistic wind speed forecasting using decomposition methods: Accuracy and uncertainty

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  • Sun, Qian
  • Che, Jinxing
  • Hu, Kun
  • Qin, Wen

Abstract

Wind energy is gaining in importance as a source of renewable energy. However, because wind speed is intermittent, integrating wind energy into the grid requires accurate forecasts. This paper tests several forecasting models based on decomposition, using data from two representative sea-land sites, Fujian and Inner Mongolia. It also analyzes the diverse uncertainty perspective of probability distributions and the role of causal factors. The main finding is that a method based on extreme machine learning achieves greater forecast accuracy than other decomposition-based methods. A further finding is that the uncertainty associated with forecasts can vary as a function of the model, and more specifically, the decomposition method. The results of experiments show that the optimal forecasting model for Fujian and Inner Mongolia wind speed is EMD-SE-WT-PSO-ELM and CEEMDAN-SE-WT-PSO-ELM respectively, with error indicator improvement rates both exceeding 27.99 %. However, in the long-term forecasts for the next 4 h, VMD-SE-WT-PSO-ELM gave the best forecasts, with goodness-of-fit above 90 % in all cases.

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  • Sun, Qian & Che, Jinxing & Hu, Kun & Qin, Wen, 2025. "Deterministic and probabilistic wind speed forecasting using decomposition methods: Accuracy and uncertainty," Renewable Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:renene:v:243:y:2025:i:c:s0960148125001776
    DOI: 10.1016/j.renene.2025.122515
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    References listed on IDEAS

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    1. Long Cai & Jie Gu & Jinghuan Ma & Zhijian Jin, 2019. "Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees," Energies, MDPI, vol. 12(1), pages 1-19, January.
    2. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    3. Zhao, Ning & Su, Yi & Dai, Xianxing & Jia, Shaomin & Wang, Xuewei, 2024. "A new decomposition-ensemble strategy fusion with correntropy optimization learning algorithms for short-term wind speed prediction," Applied Energy, Elsevier, vol. 369(C).
    4. Yang, Dongchuan & Li, Mingzhu & Guo, Ju-e & Du, Pei, 2024. "An attention-based multi-input LSTM with sliding window-based two-stage decomposition for wind speed forecasting," Applied Energy, Elsevier, vol. 375(C).
    5. Emeksiz, Cem & Tan, Mustafa, 2022. "Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach," Energy, Elsevier, vol. 238(PA).
    6. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
    7. Hou, Guolian & Wang, Junjie & Fan, Yuzhen & Zhang, Jianhua & Huang, Congzhi, 2024. "A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation," Renewable Energy, Elsevier, vol. 226(C).
    8. Bessa, Ricardo J. & Miranda, V. & Botterud, A. & Zhou, Z. & Wang, J., 2012. "Time-adaptive quantile-copula for wind power probabilistic forecasting," Renewable Energy, Elsevier, vol. 40(1), pages 29-39.
    9. Liu, Fa & Sun, Fubao & Wang, Xunming, 2023. "Impact of turbine technology on wind energy potential and CO2 emission reduction under different wind resource conditions in China," Applied Energy, Elsevier, vol. 348(C).
    10. Zheng, Yi & Wang, Jiawei & You, Shi & Li, Ximei & Bindner, Henrik W. & Münster, Marie, 2023. "Data-driven scheme for optimal day-ahead operation of a wind/hydrogen system under multiple uncertainties," Applied Energy, Elsevier, vol. 329(C).
    11. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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