Mengyuan He , Hong Liu , Shan Zhou , Yan Yao , Risto Kosonen , Yuxin Wu , Baizhan Li
{"title":"基于机器学习的温暖环境中老年人热舒适度评估:将 XGBoost 算法与人体热能分析相结合","authors":"Mengyuan He , Hong Liu , Shan Zhou , Yan Yao , Risto Kosonen , Yuxin Wu , Baizhan Li","doi":"10.1016/j.ijthermalsci.2024.109519","DOIUrl":null,"url":null,"abstract":"<div><div>Many elderly people rarely own or use air conditioners because of low income and economising habits, causing them to live in warm thermal environments when heat waves and hot weather occur. Living in warm conditions worsens thermal discomfort and poses health risks this group. To investigate the thermal comfort and adaptation of the elderly, a total of 38 participants were recruited for two parts of experiments in a climate chamber: Part A collected thermal sensation vote (TSV) and physiological parameters for 30 min at 28, 30, and 32 °C, and Part B presented a 20-min cooling with fans (air velocities of 0.6 and 1.4 m/s) at the same temperature. Furthermore, we constructed a thermal comfort model for the elderly based on human body exergy analysis and the GBDT, AdaBoost, and XGBoost machine-learning algorithms. The results showed that the predicted mean vote considerably overestimated the actual TSV. The TSV and mean skin temperature were decreased by 0.1–0.5 scores and 0.4–0.5 °C by the behavioural adaptation of fan cooling. The predictive results showed that the XGBoost model performed better, with R<sup>2</sup> score, mean absolute error (MAE), and mean squared error (MSE) of 81 %, 0.10, and 0.01. Exergy transfer from evaporation (<em>E</em><sub><em>x-Esk</em></sub>), mean skin temperature (<em>mt</em><sub><em>sk</em></sub>), air velocity (<em>v</em><sub><em>a</em></sub>), and convective exergy transfer (<em>E</em><sub><em>x-C</em></sub>) contributed more to the feature importance in the SHAP value analysis. The current study has implications for investigating physiological comfort and age-friendly environmental designs for the elderly, providing new perspectives for thermal comfort evaluations.</div></div>","PeriodicalId":341,"journal":{"name":"International Journal of Thermal Sciences","volume":"209 ","pages":"Article 109519"},"PeriodicalIF":4.9000,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-based assessment of thermal comfort for the elderly in warm environments: Combining the XGBoost algorithm and human body exergy analysis\",\"authors\":\"Mengyuan He , Hong Liu , Shan Zhou , Yan Yao , Risto Kosonen , Yuxin Wu , Baizhan Li\",\"doi\":\"10.1016/j.ijthermalsci.2024.109519\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Many elderly people rarely own or use air conditioners because of low income and economising habits, causing them to live in warm thermal environments when heat waves and hot weather occur. Living in warm conditions worsens thermal discomfort and poses health risks this group. To investigate the thermal comfort and adaptation of the elderly, a total of 38 participants were recruited for two parts of experiments in a climate chamber: Part A collected thermal sensation vote (TSV) and physiological parameters for 30 min at 28, 30, and 32 °C, and Part B presented a 20-min cooling with fans (air velocities of 0.6 and 1.4 m/s) at the same temperature. Furthermore, we constructed a thermal comfort model for the elderly based on human body exergy analysis and the GBDT, AdaBoost, and XGBoost machine-learning algorithms. The results showed that the predicted mean vote considerably overestimated the actual TSV. The TSV and mean skin temperature were decreased by 0.1–0.5 scores and 0.4–0.5 °C by the behavioural adaptation of fan cooling. The predictive results showed that the XGBoost model performed better, with R<sup>2</sup> score, mean absolute error (MAE), and mean squared error (MSE) of 81 %, 0.10, and 0.01. Exergy transfer from evaporation (<em>E</em><sub><em>x-Esk</em></sub>), mean skin temperature (<em>mt</em><sub><em>sk</em></sub>), air velocity (<em>v</em><sub><em>a</em></sub>), and convective exergy transfer (<em>E</em><sub><em>x-C</em></sub>) contributed more to the feature importance in the SHAP value analysis. The current study has implications for investigating physiological comfort and age-friendly environmental designs for the elderly, providing new perspectives for thermal comfort evaluations.</div></div>\",\"PeriodicalId\":341,\"journal\":{\"name\":\"International Journal of Thermal Sciences\",\"volume\":\"209 \",\"pages\":\"Article 109519\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Thermal Sciences\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1290072924006410\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Thermal Sciences","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1290072924006410","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Machine learning-based assessment of thermal comfort for the elderly in warm environments: Combining the XGBoost algorithm and human body exergy analysis
Many elderly people rarely own or use air conditioners because of low income and economising habits, causing them to live in warm thermal environments when heat waves and hot weather occur. Living in warm conditions worsens thermal discomfort and poses health risks this group. To investigate the thermal comfort and adaptation of the elderly, a total of 38 participants were recruited for two parts of experiments in a climate chamber: Part A collected thermal sensation vote (TSV) and physiological parameters for 30 min at 28, 30, and 32 °C, and Part B presented a 20-min cooling with fans (air velocities of 0.6 and 1.4 m/s) at the same temperature. Furthermore, we constructed a thermal comfort model for the elderly based on human body exergy analysis and the GBDT, AdaBoost, and XGBoost machine-learning algorithms. The results showed that the predicted mean vote considerably overestimated the actual TSV. The TSV and mean skin temperature were decreased by 0.1–0.5 scores and 0.4–0.5 °C by the behavioural adaptation of fan cooling. The predictive results showed that the XGBoost model performed better, with R2 score, mean absolute error (MAE), and mean squared error (MSE) of 81 %, 0.10, and 0.01. Exergy transfer from evaporation (Ex-Esk), mean skin temperature (mtsk), air velocity (va), and convective exergy transfer (Ex-C) contributed more to the feature importance in the SHAP value analysis. The current study has implications for investigating physiological comfort and age-friendly environmental designs for the elderly, providing new perspectives for thermal comfort evaluations.
期刊介绍:
The International Journal of Thermal Sciences is a journal devoted to the publication of fundamental studies on the physics of transfer processes in general, with an emphasis on thermal aspects and also applied research on various processes, energy systems and the environment. Articles are published in English and French, and are subject to peer review.
The fundamental subjects considered within the scope of the journal are:
* Heat and relevant mass transfer at all scales (nano, micro and macro) and in all types of material (heterogeneous, composites, biological,...) and fluid flow
* Forced, natural or mixed convection in reactive or non-reactive media
* Single or multi–phase fluid flow with or without phase change
* Near–and far–field radiative heat transfer
* Combined modes of heat transfer in complex systems (for example, plasmas, biological, geological,...)
* Multiscale modelling
The applied research topics include:
* Heat exchangers, heat pipes, cooling processes
* Transport phenomena taking place in industrial processes (chemical, food and agricultural, metallurgical, space and aeronautical, automobile industries)
* Nano–and micro–technology for energy, space, biosystems and devices
* Heat transport analysis in advanced systems
* Impact of energy–related processes on environment, and emerging energy systems
The study of thermophysical properties of materials and fluids, thermal measurement techniques, inverse methods, and the developments of experimental methods are within the scope of the International Journal of Thermal Sciences which also covers the modelling, and numerical methods applied to thermal transfer.