Real-world deployment of model-free reinforcement learning for energy control in district heating systems : Enhancing flexibility across neighboring buildings
Energy Management Systems (EMSs) often operate through inflexible, rule-based control systems. Model-free reinforcement learning (RL) has emerged as a promising alternative, providing adaptive and autonomous control without the need for detailed modeling. However, the complexity of operating energy systems and dynamic environmental conditions limits their practical use, with most studies relying o
