Uncertainty‐Driven Ensemble Framework for Efficient Ultra‐Short‐Term Renewable Power Prediction

ABSTRACT The rapid expansion of renewable energy and the growing role of electricity market trading have created an urgent demand for ultra‐short‐term probabilistic forecasting of wind and solar generation. Existing studies often emphasise deterministic accuracy, yet lightweight and deployment‐friendly approaches for calibrated uncertainty modelling remain scarce. This paper proposes a practical framework for ultra‐short‐term probabilistic forecasting based on a Swin Transformer backbone combined with a learnable perturbation strategy. Unlike conventional ensemble methods that require training multiple models, the proposed approach introduces adaptive sample‐level disturbances into a single deterministic predictor, enabling efficient uncertainty quantification without additional training overhead. The framework is validated on 15‐min resolution wind and solar power data from a region with diverse meteorological and spatial conditions. Results show that the disturbance‐based ensemble achieves reliable predictive intervals while preserving high deterministic accuracy, supporting risk‐aware scheduling and energy management in renewable‐rich power systems. The proposed method offers a scalable and computationally efficient alternative for uncertainty‐aware forecasting in large‐scale zero‐carbon energy applications.