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Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm

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  • He, Xingyue
  • He, Bitao
  • Qin, Tao
  • Lin, Chuan
  • Yang, Jing

Abstract

The large-scale integration of wind power to the grid poses some potential challenges to the power system. Accurate wind power forecasts reduce the impact of the nonlinearities and volatility of wind power generation. A two-channel deep learning model based on an improved coot optimization algorithm (ICOA) is proposed. First, the use of long short-term memory (LSTM) builds a channel for the extraction of chronological characteristics of historical power. Second, a temporal convolutional network (TCN) is adopted as a hybrid feature-extraction channel for multi-dimensional meteorological data, and by incorporating the self-attention mechanism (SA), the ability of TCN to extract internal information from meteorological features is enhanced. Finally, the ICOA that introduces nonlinear decision factor, adaptive dynamic boundary, and Cauchy mutation is used to optimize the model hyperparameters. The simulation analysis is carried out on the winter and summer measured data of a wind farm in Xinjiang. The results show that compared with the traditional LSTM model, the root mean square error and the mean absolute error of the proposed method are reduced by 10.35 % and 16.27 % on average, respectively, and the prediction accuracy is higher than that of other comparative models, which verifies the superiority of our proposed model.

Suggested Citation

  • He, Xingyue & He, Bitao & Qin, Tao & Lin, Chuan & Yang, Jing, 2024. "Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm," Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:energy:v:305:y:2024:i:c:s0360544224020942
    DOI: 10.1016/j.energy.2024.132320
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    References listed on IDEAS

    as
    1. 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).
    2. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    3. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
    4. Zhao, Geya & Xue, Minggao & Cheng, Li, 2023. "A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network," Resources Policy, Elsevier, vol. 85(PB).
    5. Sun, zexian & Zhao, mingyu & Dong, yan & Cao, xin & Sun, Hexu, 2021. "Hybrid model with secondary decomposition, randomforest algorithm, clustering analysis and long short memory network principal computing for short-term wind power forecasting on multiple scales," Energy, Elsevier, vol. 221(C).
    6. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
    7. 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).
    8. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    9. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    10. Jiang, Zheyong & Che, Jinxing & He, Mingjun & Yuan, Fang, 2023. "A CGRU multi-step wind speed forecasting model based on multi-label specific XGBoost feature selection and secondary decomposition," Renewable Energy, Elsevier, vol. 203(C), pages 802-827.
    11. Liu, Hui & Yu, Chengqing & Wu, Haiping & Duan, Zhu & Yan, Guangxi, 2020. "A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting," Energy, Elsevier, vol. 202(C).
    12. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    13. Zhang, Chu & Ma, Huixin & Hua, Lei & Sun, Wei & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction," Energy, Elsevier, vol. 254(PA).
    14. Wang, Han & Zhang, Ning & Du, Ershun & Yan, Jie & Han, Shuang & Li, Nan & Li, Hongxia & Liu, Yongqian, 2023. "An adaptive identification method of abnormal data in wind and solar power stations," Renewable Energy, Elsevier, vol. 208(C), pages 76-93.
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