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作 者:蔡婧仪 莫男 何卓英 韩杰 CAI Jingyi;MO Nan;HE Zhuoying;HAN Jie(Guilin University of Electronic Technology,Guilin 541004,Guangxi,China;Guangxi Engineering Research Center for Intelligent Architecture and Human Settlements,Guilin 541004,Guangxi,China)
机构地区:[1]桂林电子科技大学,广西桂林541004 [2]广西智慧建筑与人居环境工程研究中心,广西桂林541004
出 处:《建筑科学》2024年第6期72-87,共16页Building Science
基 金:“桂林电子科技大学研究生教育创新计划项目”(2023YCXS190);广西自然科学基金面上项目“广西地区混合冷却建筑居民开/关窗和开/关空调的热适应行为特征研究”(2018GXNSFAA281212)。
摘 要:基于皮肤温度的热感觉预测方法已有较长的发展历史,由于测试仪器和研究方法不断升级,新的成果不断涌现,因此有必要对基于皮肤温度的热感觉评价方法进行系统梳理和总结。本文基于Web of science数据库,回顾了近二十年来基于皮肤温度的热感觉预测方法的研究进展,结果表明:1)皮肤温度受内外多因素共同作用,手腕和面部是常用的皮肤温度测量部位,可以准确反映人体热感觉;2)基于皮肤温度的热感觉预测模型已从基于热平衡的物理模型发展为基于数据驱动的机器学习模型,其预测结果更加准确;3)基于皮肤温度的热感觉预测结果作为室内空调系统控制的数据基础,其控制效果优于基于温度设定点控制,此外,与人工智能相结合的群体热感觉预测模型是未来研究的发展方向之一。本文以期为基于皮肤温度的热感觉预测方法进一步深入研究和应用提供理论参考。The method of predicting thermal sensation based on skin temperature has developed for a long time.With continued upgrading of measurement instruments and investigation methods,new achievements keep emerging.Therefore,it is necessary to systematically review and summarize thermal sensation evaluation methods based on skin temperature.This paper reviewed the research progress of thermal sensation prediction methods based on skin temperature in the past 20 years through the Web of Science.The results indicate that:1) skin temperature is affected by multiple factors both in vivo and vitro,wrists and faces are the common parts for measuring skin temperature,which can accurately reflect human thermal sensation;2) the thermal sensation prediction models based on skin temperature have evolved from physical models based on thermal equilibrium to more accurate data-driven approaches;3) the results of thermal sensation prediction based on skin temperature have been utilized as a database for controlling indoor air conditioning systems,yielding better control outcomes than those based on temperature set point control.Looking ahead,future research on skin temperature-based thermal sensory prediction methods may focus on developing group thermal sensation prediction models that integrate artificial intelligence.This paper serves as a valuable reference for advancing the theory and application of skin temperature-based thermal sensation prediction methods.
分 类 号:TU831[建筑科学—供热、供燃气、通风及空调工程]
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