基于居住者人口特征的机器学习热舒适度预测模型

IF 2.9 2区 生物学 Q2 BIOLOGY
Ezgi Kocaman , Merve Kuru Erdem , Gulben Calis
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引用次数: 0

摘要

本研究旨在调查热感觉(TS)和热满意度(TSa)的预测性居住人口特征,并找出预测 TS 和 TSa 的最有效机器学习(ML)算法。为此,在三栋混合模式建筑中开展了一项调查活动,使用六种 ML 算法(逻辑回归、奈夫贝叶斯、决策树、随机森林、K-最近邻(KNN)和支持向量机)开发 TS 和 TSa 预测模型。预测模型是根据六个人口统计学特征(性别、年龄、热病史、教育程度、收入、职业)建立的。结果表明,性别、年龄和热历史是 TS 和 TSa 的重要预测因素。教育水平、收入和职业对 TS 的预测作用不明显,但对 TSa 的预测作用明显。研究还发现,RF 和 KNN 是预测 TS 最有效的 ML 算法,而 DT 和 RF 则是预测 TSa 最有效的 ML 算法。研究发现,TS 预测模型的准确率在 83% 到 99% 之间,中性是最正确的分类尺度。TSa 预测模型的准确率在 84% 到 97% 之间,其中不满意是最常见的错误分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning thermal comfort prediction models based on occupant demographic characteristics

This study aims to investigate the predictive occupant demographic characteristics of thermal sensation (TS) and thermal satisfaction (TSa) as well as to find the most effective machine learning (ML) algorithms for predicting TS and TSa. To achieve this, a survey campaign was carried out in three mixed-mode buildings to develop TS and TSa prediction models by using six ML algorithms (Logistic Regression, Naïve Bayes, Decision Tree (DT), Random Forest (RF), K-Nearest Neighborhood (KNN) and Support Vector Machine). The prediction models were developed based on six demographic characteristics (gender, age, thermal history, education level, income, occupation). The results show that gender, age, and thermal history are significant predictors of both TS and TSa. Education level, income, and occupation were not significant predictors of TS, but were significant predictors of TSa. The study also found that RF and KNN are the most effective ML algorithms for predicting TS, while DT and RF are the most effective ML algorithms for predicting TSa. The study found that the accuracy of TS prediction models ranges from 83% to 99%, with neutral being the most correctly classified scale. The accuracy of TSa prediction models ranges from 84% to 97%, with dissatisfaction being the most common misclassification.

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来源期刊
Journal of thermal biology
Journal of thermal biology 生物-动物学
CiteScore
5.30
自引率
7.40%
发文量
196
审稿时长
14.5 weeks
期刊介绍: The Journal of Thermal Biology publishes articles that advance our knowledge on the ways and mechanisms through which temperature affects man and animals. This includes studies of their responses to these effects and on the ecological consequences. Directly relevant to this theme are: • The mechanisms of thermal limitation, heat and cold injury, and the resistance of organisms to extremes of temperature • The mechanisms involved in acclimation, acclimatization and evolutionary adaptation to temperature • Mechanisms underlying the patterns of hibernation, torpor, dormancy, aestivation and diapause • Effects of temperature on reproduction and development, growth, ageing and life-span • Studies on modelling heat transfer between organisms and their environment • The contributions of temperature to effects of climate change on animal species and man • Studies of conservation biology and physiology related to temperature • Behavioural and physiological regulation of body temperature including its pathophysiology and fever • Medical applications of hypo- and hyperthermia Article types: • Original articles • Review articles
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