【摘要】 在建筑降碳战略背景下，暖通空调系统(HVAC)的智能化节能控制是建筑能耗优化的核心突破口。在DQN(深度Q网络)和LLM(大语言模型)的基础上，聚焦HVAC冷水子系统的智能化节能控制，构建高精度数据驱动仿真环境，设计包含多动作空间及融合能效、安全与稳定性的多目标奖励函数，系统评估不同提示词(Prompt)方案下的控制性能，通过DQN从历史数据中挖掘“类专家策略”为LLM提供决策先验，形成“思维模拟、预测分析、量化节能”一体化闭环。实验结果表明，该智能化联动框架可靠性和效果突出，较传统PID控制实现20.3%的节能率，实现节能与降碳协同增效，为建筑物运营降碳提供了可量化、可推广的技术范式。更多还原

【Abstract】 Against the backdrop of the carbon reduction strategy for the construction industry, the intelligent energy-saving control of heating, ventilation and air conditioning(HVAC) systems stands as the core breakthrough for optimizing building energy consumption. Based on Deep Q-Network(DQN) and large language model(LLM), this paper focused on the intelligent energy-saving control of the chilled water subsystem of HVAC systems, constructed a high-precision data-driven simulation environment, and designed a multi-objective reward function that incorporated a multi-action space and integrates energy efficiency, safety and stability. It systematically evaluated the control performance under different Prompt schemes, mined "expert-like strategies" from historical data via DQN to provide decision-making priors for LLM, and formed an integrated closed loop of "thinking simulation, predictive analysis and quantitative energy conservation". Experimental results show that this intelligent linkage framework features outstanding reliability and performance, achieving an energy saving rate of 20.3% compared with the traditional PID control. It realizes the synergistic improvement of energy conservation and carbon reduction, and provides a quantifiable and scalable technical paradigm for carbon reduction in building operation.更多还原