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.

Key words: triple convolutional neural network, variational mode decomposition, photovoltaic power output prediction, hourly prediction