It is estimated that about 40%-50% of total electricity consumption in commercial buildings can be attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems. Minimizing the energy cost while considering the thermal comfort of the occupants is very challenging due to unknown and complex relationships between various HVAC controls and thermal dynamics inside a building. To this end, we present a multi-agent, distributed deep reinforcement learning (DRL) framework based on Energy Plus simulation environment for optimizing HVAC in commercial buildings. This framework learns the complex thermal dynamics in the building and takes advantage of the differential effect of cooling and heating systems in the building to reduce energy costs, while maintaining the thermal comfort of the occupants. With adaptive penalty, the RL algorithm can be prioritized for energy savings or maintaining thermal comfort. Using DRL, we achieve more than 75\% savings in energy consumption. The distributed DRL framework can be scaled to multiple GPUs and CPUs of heterogeneous types.
翻译:据估计,商业建筑总耗电量的40%至50%可归因于供暖、通风和空调系统。考虑到居住者的热舒适度,将能源成本降到最低是十分困难的,因为各种HVAC控制与建筑物内热动态之间的关系不为人知且复杂。为此,我们提出了一个基于能源+模拟环境的多试剂、分布式强化学习(DRL)框架,以优化商业建筑中的HVAC。这个框架了解建筑中的复杂热动态,并利用建筑物中冷却和供暖系统的差别效应来降低能源成本,同时保持居住者的热舒适度。根据适应性处罚,RL算法可以优先用于节能或保持热舒适。我们利用DRL实现了超过75 ⁇ 的能源消耗节约。分布式的DRL框架可以扩展到多种类型GPU和CPU。