基于用户学习的智能动态热舒适控制系统

Intelligent Dynamic Thermal Comfort Control System with Users' Learning

静态的热环境易造成人体热适应能力降低,对健康不利。动态的热环境与自然环境相似更有利于用户的健康。提出一种基于用户学习的智能动态热舒适控制系统,在该系统中采用PMV(Predicted Mean Vote)作为控制目标,为了满足不同用户的需要提出个人热舒适区模糊学习算法,可根据个人偏好在线修改个人热舒适区;在计算实验的基础上提出动态热舒适控制策略,动态热舒适区包括舒适区和节能区,在动态热舒适控制中舒适区和节能区周期性交替变化。实验结果表明,该方法即满足用户的热舒适性需求,与静态热舒适控制相比节能效果明显,且对用户的健康有利。

Considering the fact that the static thermal environment is unfavorable to the human＇s health since it can reduce the ability of human＇s heat adaptation,and the dynamic thermal environment is favorable to the human＇s health as it is similar to the natural environment.A dynamical thermal comfortable control system for the inhabited environment was proposed based on users＇ learning.The thermal comfort Predicted Mean Vote（PMV） index was the control aim of the system.The fuzzy learning algorithm of personal thermal comfort zone was proposed,which modified the personal thermal comfort zone on line to meet the needs of different humans.The dynamical thermal comfort control strategy was proposed based on computational experiments.The dynamical thermal comfort zone included comfort zone and energy saving zone,which changed periodically.The experiment results demonstrated that this method could meet the human＇s thermal comfort need and reduce the energy consumption,and is favorable to the human＇s health.