针对中央空调系统机理建模困难和参数辨识工作较为复杂的问题，提出了一种 基于自适应建模和自学习机制的中央空调系统节能优化运行方法; 设计了空调系统马尔 可夫决策过程模型，采用具有双神经网络结构的强化学习算法解决学习过程中容易产生 的维数灾难和值函数过估计问题． 然后以广州市某办公建筑中央空调系统为研究对象，建 立该系统的 TＲNSYS 仿真平台，对算法的有效性进行了验证． 仿真结果表明: 该方法在满 足室内热舒适性要求的前提下，以系统能耗最小为目标，实现了系统的节能优化运行; 与 PID 控制和单神经网络强化学习控制方法相比，系统总能耗分别降低5． 36% 和 1． 64%， 非舒适性时间总占比分别减少2． 32%和1． 37%． 文中提出的强化学习控制器能够有效解 决值函数过估计问题，具有良好的鲁棒性，自适应优化能力和较好的节能效果，可为建筑 节能提供新思路

A method about energy-saving optimization operation of central air-conditioning system based on adaptive modeling and self-learning was proposed to solve the difficulties of mechanism modeling and parameters identification． The Markov decision process model of air-conditioning system was designed and the reinforcement learning algorithm with dual neural network structure was used to solve the curse of dimensionality and overestimation of value function during the learning process． A TＲNSYS simulation platform based on the central air-conditioning system of an office building in Guangzhou was built and the effectiveness of the algorithm was validated． The simulation results show that under the premise of meeting the indoor thermal comfort requirement，the energy-saving optimization operation of the system is realized with the goal of minimizing the energy consumption． Compared with PID control and single neural network reinforcement learning control，the total energy consumption of the system is reduced by 5. 36% and 1. 64%，the proportion of total uncomfortable time is decreased by 2. 32% and 1. 37%，respectively． The reinforcement learning controller proposed can effectively solve the overestimation problem． It has good robustness，self-adaption optimization capability and better energy-saving effect，and it can provide new ideas for building energy efficiency．