This study focuses on the short-term power prediction of photovoltaic power stations, aiming to address the intermittent and fluctuating problems of photovoltaic power generation, in order to improve the prediction accuracy and ensure the stable operation of the power system. Innovatively introduce the Kepler algorithm into this field, deeply analyze historical data, and mine the nonlinear relationships among various factors to lay a solid data foundation for subsequent predictions. The VMD-CNN-LSTM combined model is constructed. It is a model combining variational mode decomposition (VMD), convolutional neural network (CNN) and long short term memory network (LSTM), VMD adaptively decomposes the original power sequence based on frequency characteristics to reduce data complexity. CNN accurately extracts spatial features from the decomposed modal components; LSTM leverages its expertise in processing time series data to capture the dynamic trends of power changes, and the three work in synergy. Meanwhile, the Kepler optimization algorithm (KOA) is deeply integrated with this model to optimize the entire process of the model from data preprocessing to result correction. Verified by examples, compared with the traditional prediction model, the proposed method has significant optimization in evaluation indicators such as root mean square error and mean absolute error, which strongly proves its effectiveness and superiority. It provides an innovative idea and reliable method for the short-term power prediction of photovoltaic power stations and is of great significance for promoting the grid connection of photovoltaic power generation and the optimization of the power system.
Yu et al. (Thu,) studied this question.