Multi-Scale Graph Learning with Seasonal and Trend Awareness Electricity Load Forecasting

Accurate electricity load forecasting underpins smart-grid operation and broader economic planning. Yet multivariate load series are driven by weather, economic activity, and seasonal effects whose intertwined, scale-dependent dynamics make forecasting challenging. While graph neural networks (GNNs) capture spatio-temporal dependencies, they often underrepresent multi-scale structure. MTSGNN (multi-scale trend–seasonal GNN) is introduced to bridge this gap. MTSGNN couples a multi-scale trend–seasonal GNN module with a Hawkes-enhanced temporal decoder. The former decomposes load signals into multiple temporal scales and models cross-variable interactions at each scale, while the latter embeds a Hawkes process to capture decaying and self-/mutually exciting temporal influences. To effectively combine long-term trends with periodic variations, a Trend–Seasonal Spatio-Temporal Fusion mechanism is proposed, which jointly learns and integrates trend and seasonal representations across both space and time. MTSGNN is designed for multi-step load forecasting with a historical window of 120 time steps and a prediction horizon of 120 future steps. Evaluations on five real-world datasets demonstrate that MTSGNN consistently surpasses existing approaches for multi-step power load prediction, establishing a new benchmark in forecasting accuracy.