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A high-accuracy hybrid method for short-term wind power forecasting

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  • Khazaei, Sahra
  • Ehsan, Mehdi
  • Soleymani, Soodabeh
  • Mohammadnezhad-Shourkaei, Hosein

Abstract

In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic clustering and T2 statistic is employed for outlier detection. Effective feature selection is also carried out with the assistance of the Non-dominated Sorting Genetic Algorithm II (NSGA- ӀӀ) and the Radial Basis Function (RBF) Neural network. The evaluation of the proposed method using the data of Sotavento wind farm located in Spain demonstrates the very high accuracy of the proposed approach.

Suggested Citation

  • Khazaei, Sahra & Ehsan, Mehdi & Soleymani, Soodabeh & Mohammadnezhad-Shourkaei, Hosein, 2022. "A high-accuracy hybrid method for short-term wind power forecasting," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022684
    DOI: 10.1016/j.energy.2021.122020
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    5. Sourav Majumdar & Arnab Kumar Laha, 2024. "Diffusion on the circle and a stochastic correlation model," Papers 2412.06343, arXiv.org, revised Jan 2026.
    6. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
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    8. Kiszka, Adriana & Wozabal, David, 2025. "Stochastic dual dynamic programming for optimal power flow problems under uncertainty," European Journal of Operational Research, Elsevier, vol. 321(3), pages 814-836.
    9. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network," Energy, Elsevier, vol. 285(C).
    10. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    11. Qin, Rui & Chai, Hwa Kian & Liu, Kai & Yu, Hang & Huang, Jing, 2025. "A hierarchical multi-stage fusion deep learning framework for short-term wind power prediction," Renewable Energy, Elsevier, vol. 253(C).
    12. 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).
    13. Al-qaness, Mohammed A.A. & Ewees, Ahmed A. & Fan, Hong & Abualigah, Laith & Elaziz, Mohamed Abd, 2022. "Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting," Applied Energy, Elsevier, vol. 314(C).
    14. Li, Yanting & Wu, Zhenyu & Su, Yan, 2023. "Adaptive short-term wind power forecasting with concept drifts," Renewable Energy, Elsevier, vol. 217(C).
    15. Qu, Zhijian & Li, Jian & Hou, Xinxing & Gui, Jianglin, 2023. "A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction," Energy, Elsevier, vol. 281(C).
    16. Zhu, Yingqin & Liu, Yue & Wang, Nan & Zhang, ZhaoZhao & Li, YuanQiang, 2025. "Real-time Error Compensation Transfer Learning with Echo State Networks for Enhanced Wind Power Prediction," Applied Energy, Elsevier, vol. 379(C).
    17. 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).
    18. Li, Lue & Long, Jun & Yuan, Meilan, 2025. "Novel wind speed ensemble forecasting system based on the critic weighing principle of fuzzy information granulation and reverse mixed-frequency modeling," Energy, Elsevier, vol. 330(C).
    19. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    20. Ban, Guihua & Chen, Yan & Xiong, Zhenhua & Zhuo, Yixin & Huang, Kui, 2024. "The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer," Energy, Elsevier, vol. 290(C).
    21. Lee, Keunmin & Park, Bongjoon & Kim, Jeongwon & Hong, Jinkyu, 2024. "Day-ahead wind power forecasting based on feature extraction integrating vertical layer wind characteristics in complex terrain," Energy, Elsevier, vol. 288(C).
    22. Yang, Mao & Li, Xiangyu & Fan, Fulin & Wang, Bo & Su, Xin & Ma, Chenglian, 2024. "Two-stage day-ahead multi-step prediction of wind power considering time-series information interaction," Energy, Elsevier, vol. 312(C).

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