Precision Prediction Strategy for Renewable Energy Power in Power Systems—A Physical-Knowledge Integrated Model

To promote energy transformation and upgrading and achieve the dual carbon strategic goals, the proportion of renewable energy generation in China has been increasing annually. Among them, wind and photovoltaic power generation play a significant role in renewable energy generation due to their widely distributed energy sources, and their development has been particularly rapid. However, renewable energy generation exhibits strong volatility, and the integration of a large amount of renewable energy into the power grid poses a threat to the stable operation of the power system. Therefore, renewable energy power prediction is of great significance for the safe and stable operation of the power system. This paper proposes a physical-knowledge integrated renewable energy power prediction model. Firstly, the Fuzzy C-Means (FCM) method is used to handle missing data. Then, the variational mode decomposition (VMD) algorithm is applied to decompose the renewable energy power into high-frequency and low-frequency components. The high-frequency components are input into a Convolutional Neural Network (CNN) model, while the low-frequency components are input into a Long Short-Term Memory (LSTM) neural network for training and prediction. Finally, the effectiveness and feasibility of the proposed method in this paper are verified through real data from a certain actual power grid. After analysis, the proposed method demonstrates a prediction accuracy improvement of up to 5.71% compared to conventional approaches. Simultaneously, in high-noise interference environments, the prediction error can be reduced by up to 11.16%.