The increasing prevalence of Cyber-Physical Systems and the Internet of
Things (CPS-IoT) applications and Foundation Models are enabling new
applications that leverage real-time control of the environment. For example,
real-time control of Heating, Ventilation and Air-Conditioning (HVAC) systems
can reduce its usage when not needed for the comfort of human occupants, hence
reducing energy consumption. Collecting real-time feedback on human preferences
in such human-in-the-loop (HITL) systems, however, is difficult in practice. We
propose the use of large language models (LLMs) to deal with the challenges of
dynamic environments and difficult-to-obtain data in CPS optimization. In this
paper, we present a case study that employs LLM agents to mimic the behaviors
and thermal preferences of various population groups (e.g. young families, the
elderly) in a shopping mall. The aggregated thermal preferences are integrated
into an agent-in-the-loop based reinforcement learning algorithm AitL-RL, which
employs the LLM as a dynamic simulation of the physical environment to learn
how to balance between energy savings and occupant comfort. Our results show
that LLMs are capable of simulating complex population movements within large
open spaces. Besides, AitL-RL demonstrates superior performance compared to the
popular existing policy of set point control, suggesting that adaptive and
personalized decision-making is critical for efficient optimization in CPS-IoT
applications. Through this case study, we demonstrate the potential of
integrating advanced Foundation Models like LLMs into CPS-IoT to enhance system
adaptability and efficiency. The project's code can be found on our GitHub
repository.

应用大型语言模型的案例研究表明，模拟复杂人口群体在大型开放空间中的行为和热偏好，并将其集成到基于增强学习的代理 - 环境闭环算法中，可以在节能和居住者舒适性之间找到平衡，从而显示出远优于传统的设定点控制策略，这表明在 CPS-IoT 应用中实现自适应和个性化决策对于高效优化至关重要。