论文标题
通过全球本地策略搜索使用强化学习的网格互动多区域建筑控制
Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search
论文作者
论文摘要
在本文中，我们基于深入的增强学习方法（RL）方法开发了一个网格相互作用的多区域建筑控制器。该控制器旨在在正常条件下促进建筑运行，并要求响应事件，同时确保乘员舒适和能源效率。我们利用连续的动作空间RL配方，并设计了一个两阶段的全球RL训练框架。在第一阶段，使用无梯度RL算法进行全球快速策略搜索。在第二阶段，使用策略梯度方法进行本地微调。与最先进的模型预测控制（MPC）方法相反，所提出的RL控制器在实时操作期间不需要复杂的计算，并且可以适应非线性建筑模型。我们使用五区商业建筑以数值为单位说明控制器性能。
In this paper, we develop a grid-interactive multi-zone building controller based on a deep reinforcement learning (RL) approach. The controller is designed to facilitate building operation during normal conditions and demand response events, while ensuring occupants comfort and energy efficiency. We leverage a continuous action space RL formulation, and devise a two-stage global-local RL training framework. In the first stage, a global fast policy search is performed using a gradient-free RL algorithm. In the second stage, a local fine-tuning is conducted using a policy gradient method. In contrast to the state-of-the-art model predictive control (MPC) approach, the proposed RL controller does not require complex computation during real-time operation and can adapt to non-linear building models. We illustrate the controller performance numerically using a five-zone commercial building.
