基于组合深度学习的光伏功率日前概率预测模型OA北大核心CSTPCD

To accurately quantify the uncertainty in the predicted photovoltaic(PV)power in complex scenarios,a short-term probabilistic prediction method for PV power based on a combination of temporal convolutional networks-attention mechanism-long short-term memory networks is proposed in this paper.Firstly,mete-orological factors strongly correlated with PV power are selected based on multiple correlation analysis methods.Then,a combined deep learning prediction model is built based on the feature extraction capability of the temporal convolutional network and the temporal feature modeling capability of the long and short-term memory network,combined with the attention mechanism and quantile regression.Finally,a kernel density estimation method is used to generate a continuous probability density function.The cases of actual centralized and distributed PV plants are analyzed,and the results show that compared with long short-term memory networks,temporal convolutional networks,temporal convolutional networks-attention mechanism,and temporal convolutional networks-long short-term memory networks,the proposed method can improve the performance of probability density prediction while ensuring the optimal prediction interval.