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Short-Term Wind Power Prediction Based on a Variational Mode Decomposition–BiTCN–Psformer Hybrid Model

Author

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  • Wu Xu

    (School of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, China
    Yunnan Key Laboratory of Unmanned Autonomous System, Kunming 650504, China)

  • Wenjing Dai

    (School of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, China
    Yunnan Key Laboratory of Unmanned Autonomous System, Kunming 650504, China)

  • Dongyang Li

    (School of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, China
    Yunnan Key Laboratory of Unmanned Autonomous System, Kunming 650504, China)

  • Qingchang Wu

    (Lancang-Mekong International Vocational Institute, Yunnan Minzu University, Kunming 650504, China)

Abstract

Precise wind power forecasting is essential for the successful integration of wind power into the power grid and for mitigating the potential effects of wind power on the power system. To enhance the precision of predictions, a hybrid VMD-BiTCN-Psformer model was devised. Firstly, VMD divided the original sequence into several data components with varying time scales. Furthermore, the BiTCN network was utilized to extract the sequence features. These features, along with the climate features, were then input into the positional encoding and ProbSparse self-attention improved Transformer model. The outputs of these models were combined to obtain the ultimate wind power prediction results. For the prediction of the wind power in Fujian Province on April 26, four additional models were developed for comparison with the VMD-BiTCN-Psformer model. The VMD-BiTCN-Psformer model demonstrated the greatest level of forecast accuracy among all the models. The R 2 increased by 22.27%, 12.38%, 8.93%, and 2.59%, respectively.

Suggested Citation

  • Wu Xu & Wenjing Dai & Dongyang Li & Qingchang Wu, 2024. "Short-Term Wind Power Prediction Based on a Variational Mode Decomposition–BiTCN–Psformer Hybrid Model," Energies, MDPI, vol. 17(16), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4089-:d:1458212
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    References listed on IDEAS

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    1. Xiang, Ling & Fu, Xiaomengting & Yao, Qingtao & Zhu, Guopeng & Hu, Aijun, 2024. "A novel model for ultra-short term wind power prediction based on Vision Transformer," Energy, Elsevier, vol. 294(C).
    2. Bentsen, Lars Ødegaard & Warakagoda, Narada Dilp & Stenbro, Roy & Engelstad, Paal, 2023. "Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures," Applied Energy, Elsevier, vol. 333(C).
    3. Liu, Hongyi & Han, Hua & Sun, Yao & Shi, Guangze & Su, Mei & Liu, Zhangjie & Wang, Hongfei & Deng, Xiaofei, 2022. "Short-term wind power interval prediction method using VMD-RFG and Att-GRU," Energy, Elsevier, vol. 251(C).
    4. Zhang, Dongdong & Chen, Baian & Zhu, Hongyu & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model," Energy, Elsevier, vol. 285(C).
    5. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(C).
    6. Ye, Lin & Li, Yilin & Pei, Ming & Zhao, Yongning & Li, Zhuo & Lu, Peng, 2022. "A novel integrated method for short-term wind power forecasting based on fluctuation clustering and history matching," Applied Energy, Elsevier, vol. 327(C).
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