Abstract: Considering the volatility and uncertainty of PV power generation under different weather types,a short-term power prediction model of PV power generation based on fuzzy C-mean clustering algorithm and predator optimization algorithm to optimize the bi-directional long short-term memory network is proposed. Firstly,the PV power generation data are processed and analyzed,then the principal component analysis downscaling and FCM clustering algorithm are performed to classify the data into cloudy,sunny and rainy according to weather types. Then,the best hyperparameters of the BILSTM neural network are derived through HPO screening,which avoids the impact of poor hyperparameter settings on the experiments and further improves the accuracy of the experiments and the generalization ability of the model. Finally,the superiority of the proposed method is verified by prediction and comparison experiments.