Developing a wind power forecasting system based on deep learning with attention mechanism
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DOI: 10.1016/j.energy.2022.124750
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Cited by:
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- Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
- Zhao, Beizhen & He, Xin & Ran, Shaolin & Zhang, Yong & Cheng, Cheng, 2024. "Spatial correlation learning based on graph neural network for medium-term wind power forecasting," Energy, Elsevier, vol. 296(C).
- Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhou, Qingyu & Fan, Hang, 2023. "Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction," Applied Energy, Elsevier, vol. 336(C).
- Oliveira Santos, Victor & Costa Rocha, Paulo Alexandre & Scott, John & Van Griensven Thé, Jesse & Gharabaghi, Bahram, 2023. "Spatiotemporal analysis of bidimensional wind speed forecasting: Development and thorough assessment of LSTM and ensemble graph neural networks on the Dutch database," Energy, Elsevier, vol. 278(PA).
- Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "Dual-meta pool method for wind farm power forecasting with small sample data," Energy, Elsevier, vol. 267(C).
- 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).
- 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).
- 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).
- Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
- Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2024. "A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting," Energy, Elsevier, vol. 286(C).
- Cao, Wangbin & Wang, Guangxing & Liang, Xiaolin & Hu, Zhengwei, 2024. "A STAM-LSTM model for wind power prediction with feature selection," Energy, Elsevier, vol. 296(C).
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Keywords
Attention mechanism; Deep learning; Forecasting; Optimization; Wind power;All these keywords.
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