Imbalance-Aware Spatiotemporal Load Forecasting via Cluster-Weighted State Space Modeling

Electrical load time series exhibit strong heterogeneity across daily patterns driven by calendar effects and behavioral variability, leading many forecasting models to favor dominant weekday profiles while degrading on weekends, holidays, and transition days. This paper proposes an imbalance-aware spatiotemporal forecasting framework via a cluster-conditioned state space model. Daily load patterns are identified via time-series clustering and incorporated as conditioning covariates within a sequence-continuous selective state space models (Mamba), preserving temporal coherence without explicit sequence partitioning. A cluster-weighted training objective further mitigates pattern imbalance while avoiding future-information leakage. The resulting cluster-conditioned Time Series Mamba (TSMamba) consistently improves forecasting robustness across both frequent and infrequent profiles, achieving weighted absolute percentage error (WAPE) reductions of approximately 15% on weekdays, 42% on weekends, and 39% on holidays relative to the vanilla TSMamba, with similar gains in mean absolute error (MAE) and coefficient of variation of the root mean square error (CVRMSE). These results demonstrate that conditioning state dynamics on latent load patterns yields stable and computationally efficient short-term load forecasts under profile transitions.