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Individual thermal comfort prediction using classification tree model based on physiological parameters and thermal history in winter 被引量:6

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摘要 Individual thermal comfort models based on physiological parameters could improve the efficiency of the personal thermal comfort control system.However,the effect of thermal history has not been fully addressed in these models.In this study,climate chamber experiments were conducted in winter using 32 subjects who have different indoor and outdoor thermal histories.Two kinds of thermal conditions were investigated:the temperature dropping(24-16℃)and severe cold(12℃)conditions.A simplified method using historical air temperature to quantify the thermal history was proposed and used to predict thermal comfort and thermal demand from physical or physiological parameters.Results show the accuracies of individual thermal sensation prediction was low to about 30%by using the PMV index in cold environments of this study.Base on the sensitivity and reliability of physiological responses,five local skin temperatures(at hand,calf,head,arm and thigh)and the heart rate are optimal input parameters for the individual thermal comfort model.With the proposed historical air temperature as an additional input,the general accuracies using classification tree model C5.0 were increased up by 15.5%for thermal comfort prediction and up by 29.8%for thermal demand prediction.Thus,when predicting thermal demands in winter,the factor of thermal history should be considered.
出处 《Building Simulation》 SCIE EI CSCD 2021年第6期1651-1665,共15页 建筑模拟(英文)
基金 This research was financially supported by the National Key Research and Development Program of China(No.2019YFE0100300-05) the Fundamental Research Funds for the Central Universities(No.2020CDCGJ027) the 111 Project(No.B13041) Academy of Finland(No.329306) The author,Yuxin Wu,would like to thank the Chinese Scholarship Council(No.201806050244)for their sponsorship of a research visiting study aboard at Aalto University in Finland.
作者简介 Hong Liu,E-mail:liuhong1865@163.com。
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  • 1范洁,杨岳湘.决策树后剪枝算法的研究[J].湖南广播电视大学学报,2005(1):54-56. 被引量:9
  • 2季桂树,陈沛玲,宋航.决策树分类算法研究综述[J].科技广场,2007(1):9-12. 被引量:42
  • 3Hunt E B, Krivanek J. The effects of pentylenetatrazole and methyl-phenoxy propane on discrimination learning[J]. Psychopharmacologia, 1966(9): 1-16.
  • 4Quinlan J R. Induction of decision trees[J]. Machine Learning, 1986(4): 81-106.
  • 5Quinlan J R. C4.5: Programs for machine learning[J]. Morgan Kaufman, 1993: 81-106.
  • 6Mehta M, Agrawal R, Rissanen J. SLIQ: A fast scalable classifier for data mining[C]//Proc Int Conf Extending Database Technology, Avignon, France, 1996: 18-32.
  • 7Shafer J, Agrawal R. A scalable parallel classifier for data mining[C]//Proc 1996 Int Conf Very Large Data Bases Bombay, India, 1996: 544-555.
  • 8Rastogi R, Shim K. Public: A decision tree classifier that integrates building and pruning[C]//Proc 1998 Int Conf Very Large Data Bases, New York, 1998: 404-415.
  • 9Quinlan J R."C5" [EB/OL). http://rulequest.com, 2007.
  • 10Quinlan J R. Bagging, boosting, and C4.5[C]//Proc of 14th National Conference on Artificial Intelligence, Portland, Oregon, 1996: 725-730.

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