基于双注意力和神经网络的光伏功率预测OA

In view of the large volatility of photovoltaics and the easy loss of important information when the timing is too long,this paper integrates the advantages of attention mechanism,convolutional neural network(CNN),and bidirectional long short term memory network(BiLSTM)to propose a photovoltaic power prediction method that combines dual attention(DA)mechanism and neural network,which is called dual attention mechanism based CNN-BiLSTM(DA-CNN-BiLSTM).First,the feature and time dual attention mechanism is introduced to independently mine the correlation between photovoltaic power and various meteorological features and historical key information,and different weights are given to each meteorological feature.Then,the prediction is conducted with CNN-BiLSTM.Finally,a prediction experiment based on a real photovoltaic power station is carried out,and results show that the prposed method can better reflect the dynamic changes of the data,and the prediction performance is significantly improved under longer prediction time scales and changeable weather conditions.