Abstract: Aiming at the problem of low accuracy of photovoltaic power generation prediction, a prediction model combining crisscross optimization algorithm and weighted Gaussian process regression algorithm (CSO-WGPR) is proposed. Firstly, the weather types are divided by weighted fuzzy clustering, and similar day samples of the same type as the forecast days are selected. Secondly, one-class support vector machine (One-Class SVM) algorithm combined with traditional gaussian process regression algorithm is used to establish a weighted Gaussian process regression model (WGPR) to reduce the adverse effects of outlier data on the prediction results. Finally, the crisscross optimization algorithm (CSO) is used to optimize the hyperparameters of WGPR to further improve the prediction accuracy of the model. Australian Alice Springs photovoltaic system is taken as an example for modeling and prediction, and the real data simulation and experimental results show that the proposed prediction model has higher prediction accuracy under sunny, cloudy, and rainy days, which verifies the effectiveness of the method.