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机译:使用深度特征提取和LSTM进行数据驱动的风速预测
Zhejiang Univ Sch Math Sci Hangzhou 310027 Zhejiang Peoples R China;
learning (artificial intelligence); weather forecasting; wind power; wind power plants; feature extraction; neural nets; pattern clustering; power engineering computing; feature selection; data-driven wind speed forecasting; deep feature extraction; high-efficiency utilisation; wind energy; management; grid-connected power systems; instability; irregularity; atmosphere system; raw historical data; deep novel feature extraction approach; numerical weather prediction data; Wind Atlas; forecasting accuracy; feature selection; proper feature extraction; LSTM neural networks; long short-term memory neural networks;
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:利用LSTM-ARIMA深度学习模型的风速预测新方法
机译:基于变分模式分解,奇异频谱分析,LSTM网络和ELM的智能多步风速深度学习模型
机译:堆积的LSTM经常性神经网络:短期风速预测的深度学习方法
机译:CNN与时间序列预测的LSTMS
机译:用于增强功能提取的多次分辨率集合LSTMS在高速时间序列中提取
机译:LSTMS对风电场绩效估算的多变量风速预测