Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 안용한 | - |
dc.date.accessioned | 2024-08-06T05:52:32Z | - |
dc.date.available | 2024-08-06T05:52:32Z | - |
dc.date.issued | 2024-05-01 | - |
dc.identifier.citation | ENERGY AND BUILDINGS, v. 310, article no. 114065, page. 1-16 | en_US |
dc.identifier.issn | 0378-7788 | en_US |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0378778824001816 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/191326 | - |
dc.description.abstract | Heating, ventilation, and air-conditioning (HVAC) systems are responsible for a considerable proportion of total building energy consumption but are also vital for improved indoor temperature comfort, indoor air quality and well-being of building occupants. Thus, developing control strategies for HVAC systems is critical for the total life cycle of any building projects. Particularly, HVAC and building operations are not stationary but are filled with fuelled by environmental dynamisms and unexpected disruptions such as users' activities, weather conditions, occupancy rate, and operation of machinery and systems. This research aims to develop and propose a strategic control learning framework for HVAC systems using the deep reinforcement learning (DRL) approach. The results show that the proposed Phasic Policy Gradient (PPG) based method is more adaptive to changes in real building's environments. Notably, PPG performs better and more reliable than the conventional method for HVAC control optimization with about 2-14% in energy consumption reduction and indoor temperature comfort enhancement, along with a 66% faster convergence rate. Overall, our findings demonstrate that our proposed DRL approach is less resource intensive and much easier than the conventional approach in deriving solutions for HVAC control optimization driven by energy efficiency and indoor temperature comfort. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00217322). | en_US |
dc.language | en_US | en_US |
dc.publisher | ELSEVIER SCIENCE SA | en_US |
dc.relation.ispartofseries | v. 310, article no. 114065;1-16 | - |
dc.subject | Deep reinforcement learning | en_US |
dc.subject | Constrained learning | en_US |
dc.subject | HVAC control | en_US |
dc.subject | Building energy modelling | en_US |
dc.subject | Human comfort | en_US |
dc.subject | Energy efficiency | en_US |
dc.title | Modelling building HVAC control strategies using a deep reinforcement learning approach | en_US |
dc.type | Article | en_US |
dc.relation.volume | 310 | - |
dc.identifier.doi | https://doi.org/10.1016/j.enbuild.2024.114065 | en_US |
dc.relation.page | 1-16 | - |
dc.relation.journal | ENERGY AND BUILDINGS | - |
dc.contributor.googleauthor | Nguyen, Anh Tuan | - |
dc.contributor.googleauthor | Pham, Duy Hoang | - |
dc.contributor.googleauthor | Oo, Bee Lan | - |
dc.contributor.googleauthor | Santamouris, Mattheos | - |
dc.contributor.googleauthor | Ahn, Yonghan | - |
dc.contributor.googleauthor | Lim, Benson T.H. | - |
dc.relation.code | 2024006347 | - |
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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