Day-ahead electricity price forecasting method integrating multi-scale hypergraph features and dual-layer transformer

Accurate forecasting of spot electricity prices is critical yet challenging due to the multi-scale temporal coupling, nonlinear volatility, and complex spatial dependencies influenced by supply-demand fluctuations, extreme weather, and transmission topology. This study proposes a novel day-ahead price forecasting model integrating multi-scale hypergraph features with a dual-layer Transformer. A hypergraph is constructed based on price trend similarity to capture spatial dependencies at local, global, and full-fusion levels. High-relevance exogenous variables are selected using the maximum information coefficient (MIC), and a two-tier Transformer separately models temporal and spatial dynamics. Spectral hypergraph convolution is introduced to generate dynamic spatial representations. The model is evaluated on real-world data from the Guangdong electricity market using both single-day and rolling forecast tasks. Compared with the second-best model, RMSE, MAE, and MAPE are reduced by 9.23%, 12.00%, and 21.74%, respectively, with R2 improved by 2.25%. Additionally, SHAP analysis quantifies feature contributions, forming a closed-loop feature selection and validation process with MIC. The results demonstrate that incorporating multi-scale dynamic modeling and spatiotemporal feature fusion can significantly enhance forecasting accuracy.