This study presents a probabilistic machine-learning framework for forecasting national final energy consumption across 156 countries. Using Natural Gradient Boosting (NGBoost), it produces full predictive distributions rather than point forecasts, drawing on GDP, population, temperature, and scenario-specific energy prices in a single pooled model that learns shared relationships while allowing for cross-country differences.
