Transformer-GNN fusion with gradient surgery for electricity price forecasting

Accurate short-term electricity price forecasting is crucial for efficient market operations and reliable power grid management. Existing methods, however, frequently struggle to model both extended temporal dependencies and the spatial correlations arising from network constraints. To address this, we present a hybrid forecasting framework that integrates a Transformer encoder for long-term temporal representation, a graph neural network (GNN) for topology-aware spatial modeling, and a PCGrad gradient surgery mechanism to mitigate training conflicts across heterogeneous components. Our architecture combines market-level signals from PJM hourly records with auxiliary grid-level physical measurements derived from an IEEE 39-bus PMU simulation through synchronized window-based alignment and an attention-based fusion module. Rather than treating the IEEE 39-bus system as a literal topological surrogate of PJM, the simulated PMU stream is used as a physically informed auxiliary modality for methodological validation of cross-domain spatiotemporal fusion. Evaluated on these two datasets, the proposed model achieves improved performance over classical and hybrid benchmarks in the fused-data setting. Across five independent runs, the proposed model attains the lowest average error among the compared fused-data methods and shows narrower uncertainty intervals than the strongest multimodal baseline. These results support the claim that temporally aligned fusion of market signals and auxiliary physical features can improve forecasting in this proof-of-concept setup, while not implying reconstruction of PJM-specific physical topology.