Open access
Aug 2026
Hierarchical Reinforcement Learning for Integrated Energy System Scheduling Based on Large Language Model Forecasting
An imitation-learning-based hierarchical proximal policy optimization strategy is developed to decompose the scheduling task into system-level energy coordination and device-level action execution, which achieves the fastest convergence compared with the three benchmark methods.
Ruo-Xu Zhao, Xuan Tan, Hui Wei et al.
· Energies · 0 citations