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Solar energy is rich in resources and widely distributed, and has been widely used in various applications. Photovoltaic power generation has become a reliable, feasible and extensible and important way to use renewable energy. Therefore, accurate prediction of photovoltaic output is of great significance. A year's photovoltaic power generation data and meteorological factors were obtained from a photovoltaic power station in Ningxia, and the data from April to May were selected for research and prediction. The convergence time of BP neural network is long and it is easy to fall into local minimum. A single BP neural network prediction model was established, gA-BP prediction model based on genetic algorithm (GA) optimization of BP neural network and WPA-BP prediction model based on Wolf pack algorithm (WPA) optimization of BP neural network were established. Average relative error is chosen as the error evaluation index, the result shows that the three kinds of prediction model can forecast photovoltaic power station of power, but a single BP neural network model of the error is bigger, sunny days, the error is 5.1%, the genetic algorithm improved the prediction error is 4.9%, increased by 0.2% than that of single model accuracy, and WPA - BP prediction model error is 4.4%, the prediction accuracy is higher than the former. At the same time, when it is cloudy and rainy, the PREDICTION error of WPA-BP model is small and its stability is high, so it has certain research value.