Short-Term Forecasting of Renewable Output Using Ensemble Learning

Abstract

Accurate short-term forecasting of wind and solar output is essential for grid balancing. This paper compares ensemble machine-learning methods against classical numerical weather prediction baselines across twelve sites, reporting a 14 percent reduction in day-ahead forecast error.