Research on BiLSTM–Transformer Power Load Forecasting Method Based on Dynamic Adaptive Fusion

Power load forecasting is a core technical component for achieving safe, stable, and economic operation in smart grids. This paper proposes a hybrid BiLSTM–Transformer forecasting method based on a Dynamic Adaptive Fusion (DAF) module. The core of this method involves utilizing the DAF module to adaptively weight different feature channels to highlight key influencing factors, while simultaneously employing a temporal attention mechanism to capture the contributions of various time steps. Building on this, the model effectively combines the strengths of BiLSTM networks in capturing bidirectional dependencies with the capability of Transformer models to extract global contextual features, thereby achieving a multi-level dynamic fusion of load characteristics. Experiments on real-world grid datasets demonstrate that the proposed method achieves a significant performance improvement over traditional models, particularly in terms of load peak prediction accuracy and stability. This provides effective technical support for the refined scheduling of power systems.