基于Stacking模型融合的光伏发电功率预测

为了提高光伏发电输出功率的预测精度和可靠性，本文提出一种基于Stacking模型融合的光伏发电功率预测方法.选取某光伏电站温度、湿度、辐照度等历史实测数据为研究对象，在将光伏发电功率数据进行特征交叉以及基于模型的递归特征消除法进行预处理和特征选择的基础上，以XGBoost、LightGBM、RandomForest 3种机器学习算法作为Stacking集成学习的第一层基学习器，以LinearRegression作为第二层元学习器，构建了多个机器学习算法嵌入的Stacking模型融合的光伏发电功率预测模型.预测结果表明，该方法的R²、MSE分别达到了0.9874和0.1056，相较于单一的机器学习模型，预测精度显著提升.

Photovoltaic Power Prediction Based on Stacking Model Fusion

In order to improve the prediction accuracy and reliability of photo voltaic power prediction output, this study proposes a photo voltaic power prediction method based on Stacking model fusion. The historical measured data such as temperature, humidity, and irradiance of a PV power plant are selected as the research object. Based on the feature intersection of the photo voltaic power data and the pre-processing and feature selection based on the model-based recursive feature elimination method, XGBoost and LightGBM are used. The three machine learning algorithms of Random Forest are the first layer of base learning for Stacking integrated learning. Linear Regression is used as the second layer of element learner to construct a photo voltaic power prediction model with multiple stacking models embedded in machine learning algorithms. The prediction results show that the R² and MSE of the method reach 0.9891 and 0.1358, respectively, and the prediction accuracy is significantly improved compared with the single machine learning model.