The existing fusion methods of foundation cloud maps and meteorological factors are difficult to fully explore the correlation and complementarity between the two modes

resulting in the inability to effectively improve the accuracy of photovoltaic power prediction under severe irradiance changes in weather. In response to the above issues

this article proposes a photovoltaic power prediction method based on multimodal fusion of ground cloud maps and meteorological factors. Adopting an improved Transformer network by replacing the position encoding of the meteorological data input with a time series convolutional network(TCN) to enhance the network's feature extraction capability for meteorological data; By replacing the multi head attention mechanism module in the network decoder with a long short-term memory networks(LSTM) network module

the network's ability to predict temporal sequences is improved. By introducing guided attention mechanism and low rank multimodal fusion algorithm separately

further feature extraction and fusion of cloud map features and meteorological features can fully utilize the correlation and complementarity between different source data. The simulation results show that the average root mean square error (RMSE)

mean absolute error (MAE)

and R2 values of the above method are 0.294

0.248

and 0.866 respectively

which improving the accuracy of photovoltaic power prediction under severe irradiance changes in weather.