Integrating large language models, reinforcement learning, and machine learning for intelligent indoor thermal comfort regulation

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This paper explores a method for integrating large language models (LLMs), reinforcement learning, and machine learning models within multi-agent systems to regulate indoor thermal comfort. Utilizing natural language processing techniques, LLMs interpret user inputs, invoke pre-trained reinforcement learning models and machine learning models, predict current thermal comfort levels, and suggest appropriate actions. The study aims to enhance interaction between individuals and their indoor thermal environment. We selected a publicly available dataset as the foundation of our research. We trained a regression model and a reinforcement learning model using this dataset, integrating them into a multi-agent system's function library for intelligent management of indoor thermal comfort. Small-parameter LLMs were selected to build the natural language processing module and function calling module within the multi-agent system. When users input their current thermal feelings or environmental parameters in natural language, the LLMs can call the pre-trained models to provide suitable action suggestions. Abbreviations: AI: Artificial Intelligence; HVAC: Heating, Ventilation, and Air Conditioning; LLMs: Large Language Models; MAS: Multi-agent System; ML: Machine Learning; RL: Reinforcement Learning; PDD: Predicted Percentage of Dissatisfied; PMV: Predicted Mean Vote; PPO: Proximal Policy Optimization; TAV: Thermal Acceptability Vote; TCV: Thermal Comfort Vote; TSV: Thermal Sensation