This project propose, the solar photovoltaic (PV) permeability in power system, the impact of random fluctuation of PV power on the safe and stable operation of power grid becomes more and more serious. High-precision PV power forecasting can effectively promote the grid’s absorption of PV power generation. Cloud is the most important factor affecting the surface irradiance and PV power. For the ultra-short-term solar PV power forecast considering the influence of cloud movement, it is necessary to be able to obtain the surface irradiance according to the sky cloud observation data. Therefore, in order to accurately achieve the real-time mapping relationship between sky image and surface irradiance, a hybrid mapping model based on deep learning applied for solar PV power forecasting is proposed in this project. First, the sky image data is clustered based on the feature extraction of convolutional auto encoder and k-means clustering algorithm after preprocess stage. Second, a hybrid mapping model based on deep learning methods are established for surface irradiance. Finally, the simulation results are compared and evaluated with different deep learning methods (CNN, LSTM and ANN). The results show that the proposed model in this project has higher accuracy and can maintain robustness under different weather conditions.