Research on Output Prediction Method of Large-Scale Photovoltaic Power Station Based on Gradient-Boosting Decision Trees

As a large number of large-scale photovoltaic (PV) stations are integrated into the power grid, the penetration rate of PV power is growing higher and higher. The intermittency and volatility of PV power generation bring great pressure to the safe and stable operation of the distribution network. In order to realize scientific energy dispatching and optimization, the predicted output of large PV stations is the data basis and prerequisite. The output prediction method of large PV stations is studied in this paper, and a prediction method based on gradient-boosting decision trees is proposed. In the method, the original data are first collected, and the sample set is established through the steps of data interpolation, supplement, and integration, and then the sample set is pre-processed by data cleaning and normalization. The model training and PV output prediction during the test period are carried out based on the pre-processed data. Finally, the prediction results are imported into the error analysis module. The feasibility and accuracy of the proposed method are analyzed by comparing it with the traditional method. The results show that the normalized mean absolute error (nMAE) and normalized root mean square error (nRMSE) of the proposed method are 7.31% and 11.78%, respectively, while the nMAE and nRMSE of the traditional method are 11.67% and 20.39%, respectively. Thus, the prediction performance of the proposed method is superior to that of the traditional method.