Optimizing Electricity Demand Forecasting with Ensemble Learning: A Dynamic Factor Perspective

Accurate electricity demand forecasting is critical for efficient energy management and grid stability. This study explores the optimization of electricity demand forecasting using ensemble learning techniques, incorporating a dynamic factor perspective. By leveraging multiple machine learning models, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest, the proposed ensemble approach aims to enhance prediction accuracy and robustness. The dynamic factor model integrates weather conditions, which is a dynamic factor and historical consumption patterns, allowing for a comprehensive analysis of their impact on electricity demand. Experimental results demonstrate that the ensemble learning framework significantly outperforms traditional single-model approaches, providing more reliable and precise forecasts. This research contributes to the development of advanced forecasting methodologies, offering valuable insights for policymakers and energy providers in optimizing resource allocation and ensuring sustainable energy supply.