Prediction of photovoltaic power generation based on a hybrid model - Zhang, Xiaohua; Wu, Yuping; Wang, Yu; Lv, Zhirui; Huang, Bin; Yuan, Jingzhong; Yang, Jingyu; Ma, Xinsheng; Li, Changyuan; Zhang, Lianchao (Frontiers in Energy Research, 2024)
In order to fully exploit the relationship between temporal features in photovoltaic power generation data and improve the accuracy of photovoltaic power generation prediction accuracy, a photovoltaic power generation forecasting method is proposed based on a hybrid model of convolutional neural network (CNN) and extreme gradient boost (XGBoost). Taking the historical data of China's photovoltaic power plants as a sample, the high-dimensional mapping relationship of photovoltaic power generation variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time series feature vector, which is input the XGBoost. A photovoltaic power generation prediction model is established based on CNN-XGBoost by training CNN network and XGBoost parameters. Since it is difficult for a single model to achieve optimal prediction accuracy under different weather conditions, the k-means clustering algorithm is used to group the power datasets and train independent models to improve the prediction accuracy. Through the actual data verification of photovoltaic power plants, the proposed photovoltaic power generation prediction model can accurately predict the power, which shows high prediction accuracy and generalization ability with other methods.