Research on Short and Long-Term Prediction of New Energy Generation Power Based on VAE-GAN

New energy generation power is affected by multiple factors such as meteorological conditions, equipment status and grid dispatching demand, and its output shows strong volatility, intermittency and randomness, which brings great challenges to grid dispatching. In this paper, a new short and long-term prediction method combining Variational Autoencoder (V AE) and Generative Adversarial Nets (GAN) is proposed to solve the shortcomings of traditional models in accuracy and generalization ability. Firstly, key features highly related to power generation are extracted from multi-dimensional meteorological and load data through mutual information (MI) to ensure the stability of model input. Secondly, VAE is used to perform latent distribution learning and feature coding on historical data, and the future power sequence is generated by combining the adversarial training mechanism of GAN generator and discriminator, so as to continuously optimize the authenticity and accuracy of prediction results. The effectiveness of the proposed method is verified in short and long-term prediction tasks. Compared with BiLSTM, LightGBM, Informer and other mainstream models, the Mean Absolute Percentage Error (MAP E) of short-term prediction is reduced to 2.46%, and the coefficient of determination is increased to 0.977. The MAPE of long-term prediction is 3.37%, and the coefficient of determination is 0.961. The results show that the proposed method not only significantly improves the prediction accuracy and stability, but also has good generalization ability, which can provide reliable support for the scheduling optimization and planning decision-making of new energy generation power, and provides an innovative method exploration for new energy generation power prediction.