An Explainable Meta-Learning Framework for Adaptive Model Selection in Short-Term Load Forecasting

Abstract

Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems, particularly with the increasing integration of renewable energy and the transition toward smart grids. However, most existing approaches rely on a single forecasting model, despite evidence that model performance varies across datasets and forecasting horizons. To address this limitation, this paper proposes an explainable meta-learning framework for adaptive model selection in STLF. Unlike conventional methods that aim to identify a universally optimal model, the proposed approach learns to select the most suitable model based on dataset characteristics and forecasting conditions. The framework integrates cross-dataset evaluation, meta-feature extraction, and a Random Forest-based meta-learner to dynamically determine the best-performing model. The proposed approach is evaluated on three benchmark power systems—Panama, PJM, and Spanish datasets—under both single-step and multi-horizon forecasting settings. The results provide initial evidence of adaptability across multiple datasets. Specifically, LSTM achieves the best single-step performance on the Panama (MAPE = 2.88%) and PJM (MAPE = 7.71%) datasets, while XGBoost outperforms other models on the Spanish dataset (MAPE = 1.07%). Statistical analysis suggests meaningful performance differences, although these findings should be interpreted with caution due to the limited sample size. Furthermore, SHapley Additive exPlanations (SHAP) are employed to enhance interpretability, revealing that forecasting horizon, data variability, and dataset characteristics are the most influential factors in model selection. Overall, the proposed framework improves forecasting accuracy, robustness, and transparency, while promoting a shift from model-centric design to adaptive, data-driven model selection. The framework offers a structured and explainable approach with potential for practical deployment in smart grid applications.