Abstract: Precise photovoltaic power forecast is helpful for grid dispatching and secure operation. To enhance the forecast performance of the model, a PV prediction model combining the solar radiation model and deep learning is suggested. Firstly, the solar radiation model（SRM） is built using the solar radiation mechanism to estimate the total radiation value on the horizontal plane. Then the inclined plane radiation value received by the inclined photovoltaic panel is calculated by the inclined plane irradiance conversion method.Secondly, Pearson correlation analysis is devoted to screen out the primary factors influencing greatly photovoltaic power. Finally, the calculated value of inclined plane radiation and the major influencing factors are taken as input and derived from convolutional neural network（CNN）and long short-term memory（LSTM）network to build the PV power SRM-CNN-LSTM prediction model. Comparative experiments are carried out with the data from typical spring, summer, autumn, and winter days. The results show that the suggested method has better forecast effect compared with several other methods.