New Energy Generation Forecasting Based on Improved Transformer

As the global energy system transitions to sustainable development, efficient forecasting of new energy generation is critical for grid stability and energy optimization. However, traditional statistical and machine learning methods have limitations in capturing the spatiotemporal nonlinear characteristics of new energy generation and in coping with weather fluctuations. Therefore, this paper proposes an improved Transformer model to enhance the local feature extraction and global dependency modeling capabilities of time series data by introducing a convolutional module into the encoder and combining multi-head attention mechanism. The model uses convolutional layers to mine data trend characteristics and integrates the correlation between multivariate weather and historical power data through attention mechanisms to improve prediction accuracy. Simulation experiments show that the proposed method has excellent performance in long-period new energy generation prediction, and can accurately capture the volatility and predict the peak and valley of power generation, providing effective support for grid scheduling and energy storage strategy optimization.