A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model
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DOI: 10.1016/j.energy.2023.129189
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- Zhou, Yuan & Wang, Jiangjiang & Wei, Changqi & Li, Yuxin, 2024. "A novel two-stage multi-objective dispatch model for a distributed hybrid CCHP system considering source-load fluctuations mitigation," Energy, Elsevier, vol. 300(C).
- Zhong, Mingwei & Fan, Jingmin & Luo, Jianqiang & Xiao, Xuanyi & He, Guanglin & Cai, Rui, 2024. "InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation," Applied Energy, Elsevier, vol. 371(C).
- Gong, Bin & An, Aimin & Shi, Yaoke & Guan, Haijiao & Jia, Wenchao & Yang, Fazhi, 2024. "An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction," Energy, Elsevier, vol. 308(C).
- Izadi, Mohammad Javad & Hassani, Pourya & Raeesi, Mehrdad & Ahmadi, Pouria, 2024. "A novel WaveNet-GRU deep learning model for PEM fuel cells degradation prediction based on transfer learning," Energy, Elsevier, vol. 293(C).
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Keywords
LSTM; CNN-MRNN; Wind power forecasting; ResNet; PV power forecasting;All these keywords.
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