Short-term PV Power Prediction Based on SAGA-FCM and XGBoost

Abstract

The stable and economic operation of the power grid cannot be achieved without accurate prediction of PV plant power. To address the problem of unstable clustering quality of the fuzzy c-mean clustering algorithm, this paper proposes to combine the genetic algorithm and simulated annealing algorithm to optimize the FCM clustering algorithm, and then combine it with XGBoost to construct a short-term PV power generation prediction model. Firstly, the robustness of the clustering algorithm is improved. Secondly, the differences in power generation characteristics under different weather types are considered, and the prediction models under three typical weather types are separately trained. Finally, to verify the improvement of the model on prediction accuracy, different clustering methods are used to compare the prediction results, and the results show that the algorithm proposed in this paper has higher accuracy compared with the other three methods.