综合智慧能源2025，Vol.47Issue(6)：11-19,9.DOI:10.3969/j.issn.2097-0706.2025.06.002
结合变分模态分解与三重卷积神经网络的光伏出力预测
Photovoltaic power output prediction based on variational mode decomposition and triple convolutional neural networks
摘要
Abstract
To address the issue of low accuracy in photovoltaic power output prediction,this paper proposes a prediction model combining variational mode decomposition with triple convolutional neural networks(VMD-TCNNs).The variational mode decomposition(VMD)was adopted to decompose daily meteorological data,effectively separating intrinsic mode functions.The functions were then stitched,reconstructed and compressed into four-dimensional images,which were input into triple convolutional neural networks(TCNNs)for training and prediction.The initial prediction results from the TCNNs were further processed through fully connected layer,the dropout layer,and the regression output layer to obtain the final results.The VMD-TCNNs model was applied to predict the hourly photovoltaic power output one day in advance in Natal,Brazil.Simulations using actual photovoltaic output and meteorological data were conducted,and the results were compared with 26 other algorithms.The experimental results showed that the mean absolute error(MAE),mean square error and root mean square error of the VMD-TCNNs model were 49.05 W,7403.94 W2 and 86.05 W,respectively.Compared with other models,the evaluation indexes of the VMD-TCNNs model were lower,and its MAE value was at least 33.074%smaller than that of the other 26 models,confirming the validity of the proposed model.关键词
三重卷积神经网络/变分模态分解/光伏出力预测/每小时预测Key words
triple convolutional neural network/variational mode decomposition/photovoltaic power output prediction/hourly prediction分类
能源科技引用本文复制引用
殷林飞,张依玲..结合变分模态分解与三重卷积神经网络的光伏出力预测[J].综合智慧能源,2025,47(6):11-19,9.基金项目
国家自然科学基金项目(52107081) National Nature Science Foundation of China(52107081) （52107081）
