Flexible Reinforcement Learning Framework for Building Control using EnergyPlus-Modelica Energy Models

莫代利卡 设定值 强化学习 计算机科学 控制器(灌溉) 楼宇自动化 软件 水准点(测量) 控制工程 模拟 工程类 人工智能 操作系统 大地测量学 生物 热力学 农学 物理 地理
作者
Joon‐Yong Lee,Sen Huang,Aowabin Rahman,Amanda D. Smith,Srinivas Katipamula
标识
DOI:10.1145/3427773.3427873
摘要

In recent years, reinforcement learning (RL) methods have been greatly enhanced by leveraging deep learning approaches. RL methods applied to building control have shown potential in many applications because of their ability to complement or replace conventional methods such as model-based or rule-based controls. However, RL-based building control software is likely tailored either to one target building system or to a specific RL method so that significant additional effort would be required to customize the RL-based controller for use in other building systems or with other RL approaches. Also, RL-based building controls usually depend on building energy simulations to train controllers, so emulating building dynamics (i.e., thermal dynamics and control dynamics) and capturing sub-hourly dynamic profiles are crucial to further the development of effective RL-based building control methods. To address these challenges, we present an RL-based control software employing a high-fidelity hybrid EnergyPlus-Modelica building energy model that emulates building dynamics at 1 minute resolution. This software consists of decoupled components (environment, building emulator, control agent, and RL algorithm), which allows for quick prototyping and benchmarking of standard RL algorithms in different systems; for example, a single component can be replaced without revising the software. To demonstrate this software framework, we conducted a benchmark study using an EnergyPlus-Modelica building energy model for a Chicago office building with an RL-based controller to dynamically control the chilled water temperature setpoint and the air handling unit supply air temperature setpoints.
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