基于LightGBM-TextCNN-XGBoost的超短期光伏功率预测研究

针对超短期光伏功率传统预测方法的局限性，提出一种基于LightGBM-TextCNN-XGBoost算法模型对超短期光伏功率预测的方法。首先，将原始数据进行预处理，并通过CEEMDAN对数据进行模态分解，将模态分解后的数据归一化后基于GWO-FCM聚类算法10]对数据进行聚类为三种天气类型。其次，将数据划分，训练集对LightGBM和TextCNN算法分别进行训练，并基于Stacking思想建立基于LightGBM-TextCNN-XGBoost算法的模型进行预测，测试集用于评估模型预测效果。最后，结合R2等评价指标对预测模型进行综合评价。结果显示预测效果优良（R2=0.9766）。本文提出的模型能够精确地预测光伏发电的效率,帮助光伏电站降低损失,从而确保微电网的安全稳健运行。

Research on Ultra-Short Term Photovoltaic Power Prediction Based on LightGBM-TextCNN-XGBoost

Aiming at the limitations of traditional forecasting methods for ultra-short term photovoltaic power, a method based on LightGBM-TextCNN-XGBoost algorithm model for ultra-short term photovoltaic power forecasting is proposed. First, the original data is preprocessed, and the data is modal decomposed through CEEMDAN. After the modal decomposed data is normalized, the data is clustered into three weather types based on the GWO-FCM clustering algorithm. Secondly, the data is divided and the training set trains the LightGBM and TextCNN algorithms respectively. A model based on the LightGBM-TextCNN-XGBoost algorithm is established based on the Stacking idea to predict. The test set is used to evaluate the model prediction effect. Finally, combined with R Squared and other evaluation indexes, the prediction model is comprehensively evaluated. The results showed that the prediction effect was good (RSquared=0.9766). The model proposed in this paper can accurately predict the efficiency of photovoltaic power generation, help photovoltaic power plants reduce losses, and thus ensure the safe and robust operation of microgrids