Vincent Gbouna Zakka , Minhyun Lee , Ruixiaoxiao Zhang , Lijie Huang , Seunghoon Jung , Taehoon Hong
{"title":"Non-invasive vision-based personal comfort model using thermographic images and deep learning","authors":"Vincent Gbouna Zakka , Minhyun Lee , Ruixiaoxiao Zhang , Lijie Huang , Seunghoon Jung , Taehoon Hong","doi":"10.1016/j.autcon.2024.105811","DOIUrl":null,"url":null,"abstract":"<div><div>An efficient method for predicting occupants' thermal comfort is crucial for developing optimal environmental control strategies while minimizing energy consumption in buildings. This paper presents a non-invasive vision-based personal comfort model that integrates thermographic images and deep learning. Unlike previous studies, the entire thermographic image of the upper body is directly used during model training, minimizing complex data processing and maximizing the use of rich skin temperature distribution. The proposed method is validated using thermographic images and corresponding thermal sensation votes (TSV) from 10 participants under different experimental conditions. Results show that the model based on a 3-point TSV scale achieves exceptional classification performance with an average accuracy of 99.51 %, outperforming existing models. The model performance using a 7-point TSV scale is slightly lower, with an average accuracy of 89.90 %. This method offers potential for integrating thermal comfort models into real-time building environmental control, optimizing occupant comfort and energy consumption.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105811"},"PeriodicalIF":9.6000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524005478","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
An efficient method for predicting occupants' thermal comfort is crucial for developing optimal environmental control strategies while minimizing energy consumption in buildings. This paper presents a non-invasive vision-based personal comfort model that integrates thermographic images and deep learning. Unlike previous studies, the entire thermographic image of the upper body is directly used during model training, minimizing complex data processing and maximizing the use of rich skin temperature distribution. The proposed method is validated using thermographic images and corresponding thermal sensation votes (TSV) from 10 participants under different experimental conditions. Results show that the model based on a 3-point TSV scale achieves exceptional classification performance with an average accuracy of 99.51 %, outperforming existing models. The model performance using a 7-point TSV scale is slightly lower, with an average accuracy of 89.90 %. This method offers potential for integrating thermal comfort models into real-time building environmental control, optimizing occupant comfort and energy consumption.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.