电气技术2026，Vol.27Issue(6)：17-22,6.
基于双注意力和神经网络的光伏功率预测
Photovoltaic power prediction based on dual attention and neural networks
林仁雄 1郭凯琪 1蒋涵 1张锦泉1
作者信息
- 1. 国网福建省电力有限公司莆田供电公司,福建 莆田 351100
- 折叠
摘要
Abstract
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.关键词
注意力机制/双向长短期记忆(BiLSTM)/光伏发电功率预测Key words
attention mechanism/bidirectional long short term memory(BiLSTM)/predition of photovoltaic power generation引用本文复制引用
林仁雄,郭凯琪,蒋涵,张锦泉..基于双注意力和神经网络的光伏功率预测[J].电气技术,2026,27(6):17-22,6.
