Erik Fernando Mendez-Garces

Comparative Evaluation of Direct and Recursive Multi-Step Forecasting for Electricity Demand Using Deep Learning and Gradient Boosting Models

This study compares two multi-step forecasting strategies for 24-h horizons: a direct 24→24 strategy and a recursive strategy based on sequential 24→1 predictions, and concludes that the selection of a forecasting strategy depends on the required balance between overall accuracy, temporal stability, and computational cost.