Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 안용한 | - |
dc.date.accessioned | 2024-08-06T05:58:11Z | - |
dc.date.available | 2024-08-06T05:58:11Z | - |
dc.date.issued | 2024-04-18 | - |
dc.identifier.citation | ADVANCES IN CIVIL ENGINEERING, v. 2024, no 1, page. 1-14 | en_US |
dc.identifier.issn | 1687-8094 | en_US |
dc.identifier.issn | 1687-8086 | en_US |
dc.identifier.uri | https://onlinelibrary.wiley.com/doi/10.1155/2024/2106137 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/191328 | - |
dc.description.abstract | As demand for indoor thermal comfort increases, occupants' subjective thermal sensation is becoming an important indicator of the building environment. Traditional models like the predicted mean vote-based model may not be reliable for individual comfort. This study proposed the multihead long short-term memory (LSTM) model to reflect physical and environment-driven data variation. Controlled experiments were conducted with individual temperature measurements of six participants, and the collected data showed significant potential to predict individual thermal comfort using a model trained for each person. The results derived from this study can be utilized, in future, for predicting the thermal comfort and for optimizing the thermal environments using personal body temperature and surrounding environmental data in a space where mainly independent activities are performed. This study contributes to the relevant literature by suggesting a method that predicts thermal comfort based on the multihead LSTM method. | en_US |
dc.description.sponsorship | This work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20202020800030, Development of Smart Hybrid Envelope Systems for Zero Energy Buildings through Holistic Performance Test and Evaluation Methods and Fields Verifications). | en_US |
dc.language | en_US | en_US |
dc.publisher | HINDAWI LTD | en_US |
dc.relation.ispartofseries | v. 2024, no 1;1-14 | - |
dc.title | The Personalized Thermal Comfort Prediction Using an MH-LSTM Neural Network Method | en_US |
dc.type | Article | en_US |
dc.relation.volume | 2024 | - |
dc.identifier.doi | https://doi.org/10.1155/2024/2106137 | en_US |
dc.relation.page | 1-14 | - |
dc.relation.journal | ADVANCES IN CIVIL ENGINEERING | - |
dc.contributor.googleauthor | Cho, Jaeyoun | - |
dc.contributor.googleauthor | Shin, Hyunkyu | - |
dc.contributor.googleauthor | Ahn, Yonghan | - |
dc.contributor.googleauthor | Ho, Jongnam | - |
dc.relation.code | 2024004782 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | SCHOOL OF ARCHITECTURE | - |
dc.identifier.pid | yhahn | - |
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