In order to improve the prediction accuracy and reliability of photo voltaic power prediction output, this study proposes a photo voltaic power prediction method based on Stacking model fusion. The historical measured data such as temperature, humidity, and irradiance of a PV power plant are selected as the research object. Based on the feature intersection of the photo voltaic power data and the pre-processing and feature selection based on the model-based recursive feature elimination method, XGBoost and LightGBM are used. The three machine learning algorithms of Random Forest are the first layer of base learning for Stacking integrated learning. Linear Regression is used as the second layer of element learner to construct a photo voltaic power prediction model with multiple stacking models embedded in machine learning algorithms. The prediction results show that the R² and MSE of the method reach 0.9891 and 0.1358, respectively, and the prediction accuracy is significantly improved compared with the single machine learning model.