Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정재원 | - |
dc.date.accessioned | 2022-11-21T01:25:54Z | - |
dc.date.available | 2022-11-21T01:25:54Z | - |
dc.date.issued | 2021-10 | - |
dc.identifier.citation | INDOOR AND BUILT ENVIRONMENT, v. 30, NO. 8, article no. 1420326X20927070, Page. 1106-1123 | en_US |
dc.identifier.issn | 1420-326X;1423-0070 | en_US |
dc.identifier.uri | https://journals.sagepub.com/doi/10.1177/1420326X20927070 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/177052 | - |
dc.description.abstract | The manual control of windows is one of the common adaptive behaviours for occupants to adjust their indoor environment in homes. The cross-ventilation by the window opening provides a useful tool to control the thermal comfort and indoor air quality in homes. The objective of this study was to develop a modelling methodology for predicting individual occupant's behaviour relating to the manual control of windows by using machine learning algorithms. The proposed six machine learning algorithms were trained by the field monitoring data of 23 sample homes. The predictive performance of the machine learning algorithms was analysed. The algorithms predicted the occupant's behaviour more precisely compared with the logistic model. Among the algorithms, K-Nearest Neighbours (KNN) shows the best fitness with the monitored data set. The driving parameters of the manual control of windows in each sample home can be clearly drawn by the algorithms. The proposed machine learning algorithms can help to understand the influence of the occupant's behaviour on the indoor environment in buildings. | en_US |
dc.description.sponsorship | The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Research Foundation of Korea (NRF) with a grant funded by the Korean government (MEST) (NRF-2017R1A2B3009344). | en_US |
dc.language | en | en_US |
dc.publisher | SAGE PUBLICATIONS LTD | en_US |
dc.subject | Occupant behaviour | en_US |
dc.subject | Window opening | en_US |
dc.subject | Cross-ventilation | en_US |
dc.subject | Indoor air quality | en_US |
dc.subject | Building simulation | en_US |
dc.subject | Machine learning algorithm | en_US |
dc.title | Machine learning algorithms for predicting occupants' behaviour in the manual control of windows for cross-ventilation in homes | en_US |
dc.type | Article | en_US |
dc.relation.no | 8 | - |
dc.relation.volume | 30 | - |
dc.identifier.doi | 10.1177/1420326X20927070 | en_US |
dc.relation.page | 1106-1123 | - |
dc.relation.journal | INDOOR AND BUILT ENVIRONMENT | - |
dc.contributor.googleauthor | Park, Jun seok | - |
dc.contributor.googleauthor | Jeong, Bongchan | - |
dc.contributor.googleauthor | Chae, Young-Tae | - |
dc.contributor.googleauthor | Jeong, Jae Weon | - |
dc.sector.campus | S | - |
dc.sector.daehak | 공과대학 | - |
dc.sector.department | 건축공학부 | - |
dc.identifier.pid | jjwarc | - |
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