2025/10/09 by Kevin Steijn, Steijn, Kevin, Vamsi Priya Goli +3
Engineering · #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Physics and Society (physics.soc-ph) #Smart Grid Energy Management #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2510.08000
openalex publication_date 2025/10/09 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28
This paper presents a machine learning framework for electricity demand forecasting across diverse geographical regions using the gradient boosting algorithm XGBoost. The model integrates historical electricity demand and comprehensive weather and socioeconomic variables to predict normalized electricity demand profiles. To enable robust training and evaluation, we developed a large-scale dataset spanning multiple years and countries, applying a temporal data-splitting strategy that ensures benchmarking of out-of-sample performance. Our approach delivers accurate and scalable demand forecasts, providing valuable insights for energy system planners and policymakers as they navigate the challenges of the global energy transition.