基于气候特征分析及改进 XGBoost 算法的中长期光伏电站发电量预测方法

The importance of photovoltaic (PV) power in the energy structure is constantly highlighted, and improving the accuracy of PV power prediction has become a key issue in current research. To address the PV prediction problem, a medium- and long-term PV power generation prediction method using climate prediction data is proposed. First, multiple sub-models are divided according to the characteristics of climate prediction data and prediction period to make full use of the data. After data pre-processing, the high-value information of climate features is fully exploited through the derivation and crossover and selection of climate features. A two-fold multi-stage hyper-parameter optimization strategy is adopted to optimize the prediction model by adjusting the XGBoost hyper-parameters. Using real photovoltaic generation data, the prediction level is evaluated by MAPE, and the effectiveness of the proposed medium- and long-term PV power generation prediction method is verified by experiment. The results show that the method can effectively improve the prediction accuracy of PV power generation.