A large language model-based agent framework for simulating building users’ air-conditioning setpoint adjustment behavior under demand response

Agent-based modeling (ABM) is a powerful tool for simulating building users’ dynamic behavior in demand response (DR) programs. However, ABM faces several challenges, particularly in encoding building users’ natural language features and common sense into rules or mathematical equations. To overcome these limitations, this paper proposes an agent framework based on large language models (LLMs) to simulate building users’ air-conditioning setpoint adjustment behavior under DR. This framework leverages LLMs’ natural language processing capabilities to replicate building users’ reasoning and decision making processes. It consists of five modules: persona, perception, decision, reflection, and memory. Agents are assigned diverse personas through natural language descriptions based on empirical survey data. LLMs drive agents to reason and make decisions based on incentive prices and historical experiences. The results show that the LLM-based agent has common sense derived from natural language-defined personas and exhibits human-like irrational characteristics. This demonstrates the feasibility of replacing rules with natural language in ABM. The LLM-based agent can more effectively model hard-to-parameterize human features and provide decision explanations through LLM outputs. The results show that the inclusion of reflection and memory modules enables the agent to learn from previous decisions and reduce unreasonable choices.