Uncertainty‐Quantified Probabilistic Forecasting and Reinforcement Learning for Short‐Term Hydropower Scheduling

ABSTRACT Short‐term hydropower scheduling is inherently affected by uncertainties in both inflow and electricity demand, which challenge the reliability of management strategies. Deterministic approaches struggle to maintain feasible and economically efficient schedules, especially when a rare or extreme scenario occurs. In this paper, an integrated framework combining probabilistic forecasting with safe reinforcement learning is proposed to enhance the short‐term hydropower scheduling strategy. LightGBM and temporal fusion transformer are combined into a multimodel forecasting layer to generate calibrated probabilistic predictions. The forecasts are transformed into risk‐aware constraints for a deep reinforcement learning agent to optimise reservoir operations. Experiments under three different scenarios are conducted to demonstrate the effectiveness of the proposed framework. Probabilistic forecasting provides well‐calibrated uncertainty bounds that adapt to both stationary and highly volatile conditions. The proposed approach can achieve higher cumulative rewards, maintain operational feasibility under compound disturbances and exhibit strong adaptability to nonstationary and biased forecasting regimes.