The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions
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DOI: 10.1016/j.apenergy.2022.119794
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Cited by:
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- Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
- Gao, Huanxiang & Hu, Gang & Zhang, Dongqin & Jiang, Wenjun & Ren, Hehe & Chen, Wenli, 2024. "Prediction of wind fields in mountains at multiple elevations using deep learning models," Applied Energy, Elsevier, vol. 353(PA).
- Xiuting Guo & Changsheng Zhu & Jie Hao & Lingjie Kong & Shengcai Zhang, 2023. "A Point-Interval Forecasting Method for Wind Speed Using Improved Wild Horse Optimization Algorithm and Ensemble Learning," Sustainability, MDPI, vol. 16(1), pages 1-26, December.
- Dong, Fuxiang & Wang, Jiangjiang & Xu, Hangwei & Zhang, Xutao, 2024. "A robust real-time energy scheduling strategy of integrated energy system based on multi-step interval prediction of uncertainties," Energy, Elsevier, vol. 300(C).
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Keywords
Wind energy system; SCADA data; Short-term prediction; Neural networks; Attention mechanism;All these keywords.
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