Month-Conditioned Boosting Framework with SHAP-in-the-Loop for Short-Term Electricity Load Forecasting

Accurate short-term load forecasting is essential for reliable power system operation, particularly under the increasing uncertainty caused by abnormal weather and socio-economic fluctuations. This study presents a month-conditioned boosting framework that integrates SHapley Additive Explanations (SHAPs) into model refinement. A baseline XGBoost model was first compared with linear and tree-based regressors, followed by enhancements through lagged and rolling-window features as well as loss weighting for vulnerable months. To further improve the performance, SHAP analysis was employed to identify the dominant error-contributing features, which guided the construction of targeted month-specific interaction terms for retraining. Experimental results based on rolling-origin cross-validation showed that this approach significantly reduced the RMSE and MAPE, particularly during high-variance summer months. Moreover, the SHAP interpretation revealed the varying roles of seasonal demand structures and socio-economic mobility, thereby enhancing transparency and operational insight. The proposed framework demonstrated that embedding explainability into the learning loop improved predictive accuracy and ensured interpretability, offering a data-driven solution for electricity demand forecasting in practical settings.