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Short-term electric load forecasting is essential to the secure and stable operation and economic dispatch of power systems, and its accuracy directly affects grid dispatch decisions and operational efficiency. To address the inadequate modeling of heterogeneity among historical exogenous variables, the underutilization of known future calendar information, and the difficulty of correcting local biases in multi-step forecasts, this paper proposes KCD-GEIRTimeXer, a short-term electric load forecasting method built upon TimeXer. Before exogenous variable embedding, a Grouped Exogenous Importance Recalibration (GEIR) module is introduced. Historical exogenous variables are first grouped a priori according to their sources and physical meanings. Importance scores are then computed at the feature, variable-group, and time-step levels, and the corresponding exogenous variable weights are obtained through a bounded residual gating mechanism. At the prediction stage, a Known Calendar–Causal Dual Correction (KCD) module is further incorporated. The module refines the initial forecasts using known future calendar variables and causal statistical features derived exclusively from historical load observations, thereby mitigating local biases in multi-step forecasting. Experiments on the publicly available Panama electricity load dataset demonstrate that the proposed model outperforms all baseline models. Averaged over three random seeds, KCD-GEIRTimeXer reduces MSE, RMSE, MAE, and MAPE by 26.25%, 14.12%, 13.10%, and 12.91%, respectively, compared with TimeXer. Full article