节能2026，Vol.45Issue(3)：60-65,6.DOI:10.3969/j.issn.1004-7948.2026.03.013
基于Q-learning的空气源热泵制冷系统无模型优化控制在传感器误差下的鲁棒性分析
Analysis of the robustness of model-free optimal control for air source heat pump refrigeration system based on Q-learning under sensor error
陈文嘉 1李铮伟 1李振海1
作者信息
- 1. 同济大学机械工程与机器人学院,上海 200092
- 折叠
摘要
Abstract
The operation of HVAC systems is highly dependent on sensor data,and measurement errors can significantly affect the optimization effect and stability of control strategies.Taking the air source heat pump(ASHP)system of an office building in Shanghai City as the research object,a simulation environment is constructed based on real operation data,and different levels of sensor noise errors are introduced to quantitatively analyze their impact on the performance of model-free optimization control based on Q-learning and model-based optimization control based on particle swarm optimization(PSO).The results show that within the range of conventional sensor errors in industry,both optimization methods can achieve an improvement in system energy efficiency,with Q-learning performing better in terms of overall utility stability.Under fault-level sensor noise conditions,the performance of PSO deteriorates,but Q-learning still maintains better robustness.关键词
强化学习/无模型控制/空气源热泵/传感器测量误差Key words
reinforcement learning/model-free control/air-source heat pump/sensor measurement error分类
建筑与水利引用本文复制引用
陈文嘉,李铮伟,李振海..基于Q-learning的空气源热泵制冷系统无模型优化控制在传感器误差下的鲁棒性分析[J].节能,2026,45(3):60-65,6.
