STGFM: A Spatio-Temporal Global Factor Model for Multi-Enterprise Electricity Load Forecasting

Accurate multi-series multi-step load forecasting requires exploiting shared dynamics across series while preserving series-specific heterogeneity. However, cross-series associations evolve across forecasting instances, making fixed shared representations insufficient for these dual objectives. We propose a spatio-temporal global factor model for multi-series load forecasting, termed STGFM. A cross-series Transformer derives input-conditioned entmax aggregation weights that project historical loads onto compact time-indexed factors. A Mamba state-space module encodes historical factor dynamics, whose final representation is combined with known future covariates to generate multi-step factor forecasts. A conditional decoder then reconstructs series-specific trajectories from the predicted factors, series context, and forecast-step information. Experiments on the Real-world Load Dataset and the UCL Electricity Dataset show that STGFM reduces forecasting errors across multiple horizons. On the Real-world Load Dataset, STGFM reduces RMSE by 2.0%–7.0% across four horizons (12, 24, 48, and 96 hours) compared with TimesNet. On the UCL Electricity Dataset, the corresponding reductions are 8.2%–21.3% at 24, 48, and 96 hours relative to the strongest baseline at each horizon. Ablation studies further show that conditional reconstruction consistently reduces both RMSE and MAE compared with shared MLP decoding across both datasets and all four horizons.