基于组合损失函数的BP神经网络风力发电短期预测方法
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刘芳,汪震,刘睿迪,王锴
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Short-term forecasting method of wind power generation based on BP neural network with combined loss function
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Fang LIU,Zhen WANG,Rui-di LIU,Kai WANG
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表 2 模型的预测性能对比 |
Tab.2 Comparison of prediction performance |
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训练集样本数 | 模型 | $ {{{\varepsilon}} _{{\bf{MAE}}}} $ /kW | $ {{{\varepsilon}} _{{\bf{RMSE}}}} $ /kW | 训练时间/s | 900 | BP-ANN | 131.9 | 150.5 | 1.8 | LSTM | 154.0 | 179.1 | 5 | 本文 | 110.0 | 133.9 | 3.1 | 1800 | BP-ANN | 126.2 | 144.4 | 3.0 | LSTM | 222.9 | 259.7 | 16.5 | 本文 | 131.7 | 141.6 | 4.5 | 4500 | BP-ANN | 152.6 | 186.8 | 5.4 | LSTM | 120.9 | 147.5 | 26.6 | 本文 | 118.1 | 144.1 | 8.8 | 7200 | BP-ANN | 133.3 | 185.2 | 8.3 | LSTM | 131.6 | 161.5 | 42.9 | 本文 | 94.5 | 105.2 | 13.8 | 9000 | BP-ANN | 125.9 | 187.7 | 10.2 | LSTM | 109.2 | 130.9 | 78.8 | 本文 | 103.2 | 128.9 | 16.6 |
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