短期风力发电负荷预测的新方法

以陆上风力发电负荷数据作为研究对象，将注意力机制引入双向长短期记忆与卷积神经网络（CNN）的混合模型来预测短期电力负荷．结果显示:1）注意力机制通过对不同时步的输入进行加权，能够显著提升双向长短期记忆网络的预测性能；2）双向长短期记忆网络-CNN结构比CNN-双向长短期记忆网络结构更适用于短期负荷预测，前者相较后者能够充分利用时序信息，不会在输入初期就丢失关键信息；3）基于注意力机制的双向长短期记忆网络-CNN混合模型的均方根误差（RMSE）、平均绝对百分比误差（MAPE）分别达到了575.35和7.02%，比次佳模型（基于注意力机制的双向长短期记忆网络-CNN混合模型）分别降低了2.75%和9.65%，其在风电短期负荷预测方面有很好的应用前景． 

A new method to predict short-term load of wind power

Onshore wind power load data at Valencia, Spain (from January 1, 2015 to December 31, 2018) was analyzed in a hybrid model of bidirectional long-term/short-term memory and convolutional neural network (BiLSTM-CNN) with attention mechanism, to predict short-term power load．The attention mechanism was found to significantly improve predictive performance of BiLSTM after weighting input at varied time steps．LSTM-CNN was found more suitable for short-term load forecasting than CNN-LSTM, which could make full use of time series information but did not lose key information at the beginning．The root mean square error (RMSE) and mean absolute percentage error (MAPE) of the BiLSTM-attention-CNN model were 575.35 and 7.02%, respectively．Compared with other models, for MAPE, BiLSTM-attention-CNN was 9.65% lower than the second-best model of CNN-BiLSTM-attention; for RMSE, BiLSTM-attention-CNN was 2.75% lower than the second-best model of CNN-BiLSTM-attention．It is concluded that the present work can be readily applied in short-term forecasting of wind power load．