基于多特征提取-卷积神经网络-长短期记忆网络的短期风电功率预测方法
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匡洪海, 郭茜
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Short-Term Wind Power Prediction Method Based on Multimodal Feature Extraction-Convolutional Neural Network-Long-Short Term Memory Network
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Honghai KUANG, Qian GUO
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表5 各风电功率预测模型误差对比
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Tab. 5 Comparison of errors of different wind power prediction models
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| 类别 | 模型 | eMSE/ MW | eRMSE/ MW | eMAE/ MW | eMRE/ % |
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| 1 | ARIMA | 5.043 8 | 2.245 8 | 1.711 2 | 17.350 | | FRNN | 3.925 9 | 1.981 4 | 1.550 0 | 16.979 | | LSTM | 3.754 9 | 1.937 8 | 1.465 3 | 15.784 | | CVMD-SE-MCC-LSTM | 3.672 5 | 1.916 4 | 1.500 0 | 13.154 | | MFE-CNN-LSTM | 3.575 5 | 1.890 9 | 1.338 2 | 11.663 | | 2 | ARIMA | 4.405 7 | 2.099 0 | 1.613 6 | 13.061 | | FRNN | 3.856 1 | 1.963 7 | 1.575 9 | 17.328 | | LSTM | 3.952 9 | 1.988 2 | 1.579 7 | 16.878 | | CVMD-SE-MCC-LSTM | 3.787 2 | 1.946 1 | 1.494 7 | 12.128 | | MFE-CNN-LSTM | 3.651 9 | 1.911 0 | 1.479 8 | 16.747 | | 3 | ARIMA | 8.418 5 | 2.901 5 | 1.956 1 | 22.777 | | FRNN | 7.360 8 | 2.713 1 | 1.883 4 | 25.111 | | LSTM | 7.012 9 | 2.648 2 | 1.812 9 | 21.634 | | CVMD-SE-MCC-LSTM | 6.662 9 | 2.581 3 | 1.955 3 | 23.920 | | MFE-CNN-LSTM | 6.389 6 | 2.527 8 | 1.641 9 | 17.332 |
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