An efficient short-term wind speed prediction model based on cross-channel data integration and attention mechanisms
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DOI: 10.1016/j.energy.2022.124569
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Cited by:
- Zhang, Yi-Ming & Wang, Hao, 2023. "Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting," Energy, Elsevier, vol. 278(PA).
- Jingming Su & Xuguang Han & Yan Hong, 2023. "Short Term Power Load Forecasting Based on PSVMD-CGA Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
- Sun, Xiaojun & Yao, Chong & Song, Enzhe & Liu, Zhijiang & Ke, Yun & Ding, Shunliang, 2023. "Novel enhancement of energy distribution for marine hybrid propulsion systems by an advanced variable weight decision model predictive control," Energy, Elsevier, vol. 274(C).
- Yan Hong & Ding Wang & Jingming Su & Maowei Ren & Wanqiu Xu & Yuhao Wei & Zhen Yang, 2023. "Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model," Sustainability, MDPI, vol. 15(14), pages 1-28, July.
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Keywords
Wind speed prediction; Deep learning; Convolutional neural network; Long short-term memory; Attention mechanism;All these keywords.
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