短期风力发电负荷预测的新方法

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

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.

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 CNNBiLSTM-attention;for RMSE,BiLSTM-attention-CNN was 2.75%lower than the second-best model of CNNBiLSTM-attention.It is concluded that the present work can be readily applied in short-term forecasting of wind power load.