Short-term photovoltaic output prediction based on spatial downscaling NWP data and CNN-iTransformer-LSTM model

To enhance the accuracy of short-term photovoltaic power output prediction and address issues such as insufficient spatial resolution of meteorological forecast data and weak generalization ability of models, this paper proposes a prediction method that integrates spatial downscaling meteorological data with a convolutional neural network (CNN)-iTransformer-long short-term memory (LSTM) model. First, the rime-optimized random forest regression algorithm (RIME-RF) is employed to perform spatial downscaling on numerical weather prediction (NWP) data, thereby improving its local applicability. Second, a CNN-iTransformer-LSTM hybrid prediction model is constructed. This model utilizes a CNN as a spatial feature extractor to capture local patterns in meteorological data, employs an iTransformer to model the global dependencies among multiple variables, and leverages an LSTM to enhance the learning of short-term temporal dynamic features, thereby achieving efficient collaborative mining of multi-scale features. Finally, experiments are conducted using actual data from a photovoltaic power station in Hebei, China, during various seasons and weather conditions. The results show that the proposed model outperforms the comparison models in terms of the root mean square error (RMSE), mean absoluteerror (MAE), and R², maintaining high prediction accuracy and stability even under complex weather conditions such as overcast and rainy days. The downscaling process further enhances the prediction performance, verifying the effectiveness and practicality of this method.