机译:具有双向LSTM的深度连接剩余网络,用于一小时前进风力预测
Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea;
SK Telecom Seoul 04539 South Korea;
Korea Electrotechnol Res Inst KERI Adv Power Grid Res Ctr Uiwang 16029 South Korea;
Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea;
Univ New South Wales Sch Elect Engn & Telecommun Kensington NSW 2052 Australia;
Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea;
Forecasting; Wind power generation; Logic gates; Predictive models; Residual neural networks; Wind forecasting; Statistical analysis; Activation function; bidirectional learning; deep learning; long short-term memory; residual networks; wind power forecasting;
机译:基于分解的混合风速预测模型,使用深双向LSTM网络
机译:可再生功率预测的深度学习:使用LSTM神经网络的方法
机译:可再生功率预测的深度学习:使用LSTM神经网络的方法
机译:环境因素交通流预测中的深度双向和单向LSTM神经网络
机译:预测风电渗透率高的市场中的剩余需求。
机译:一种新的端到端方法以预测基于双向LSTM和残差神经网络的RNA二级结构谱
机译:具有双向LSTM的基于基于网络的基于网络的Prepsion层,用于Covid-19的诊断和分类