Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169017
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYang, Shiyuen_US
dc.contributor.authorChen, Wanyuen_US
dc.contributor.authorWan, Man Punen_US
dc.date.accessioned2023-06-27T02:54:40Z-
dc.date.available2023-06-27T02:54:40Z-
dc.date.issued2023-
dc.identifier.citationYang, S., Chen, W. & Wan, M. P. (2023). A machine-learning-based event-triggered model predictive control for building energy management. Building and Environment, 233, 110101-. https://dx.doi.org/10.1016/j.buildenv.2023.110101en_US
dc.identifier.issn0360-1323en_US
dc.identifier.urihttps://hdl.handle.net/10356/169017-
dc.description.abstractModel predictive control (MPC) for building energy management has exhibited a huge potential for largely cutting down energy use and improving human comfort. However, the high demand for computational power for solving the optimization is challenging the widespread deployment of MPC in real buildings. Typical MPC employs a time-triggered mechanism (TTM) that runs optimization recurrently at each time step without considering the necessity, which could cause excessive computation resource usage. This paper proposes an event-triggered mechanism (ETM) that only runs optimization when triggering events occur. Contrary to conventional ETMs that only considers the current information (e.g., room conditions), the ETM proposed is based on a cost function that covers the past, current, and future information. Based on the proposed ETM, a machine-learning-based event-triggered model predictive control (ETMPC) system that optimizes both building energy efficiency and thermal comfort is developed. The developed ETMPC system is then employed for air-conditioning control for performance evaluation through simulations. The control performance of the proposed ETMPC is compared to an MPC employing TTM and a common thermostat. Compared to the MPC employing TTM, the proposed ETMPC reduced the computational load in terms of the number of optimization runs by 77.6%–88.2%, meanwhile, achieving similar energy savings (more than 9.3% energy saving over the thermostat) as the MPC employing TTM does (9% energy saving over the thermostat). With the significant reduction of computational load, the ETMPC achieves similar thermal comfort performance as the MPC employing TTM does, and it is significantly better over the thermostat.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF2016IDM-TRANS001-031en_US
dc.relation.ispartofBuilding and Environmenten_US
dc.rights© 2023 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleA machine-learning-based event-triggered model predictive control for building energy managementen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en_US
dc.identifier.doi10.1016/j.buildenv.2023.110101-
dc.identifier.scopus2-s2.0-85148325600-
dc.identifier.volume233en_US
dc.identifier.spage110101en_US
dc.subject.keywordsEvent-Triggered Mechanismen_US
dc.subject.keywordsEvent-Triggered Mechanismen_US
dc.description.acknowledgementThis research is supported by the National Research Foundation (NRF) of Singapore through grant no. NRF2016IDM-TRANS001-031.en_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:ERI@N Journal Articles
MAE Journal Articles

SCOPUSTM   
Citations 20

11
Updated on Jul 14, 2024

Page view(s)

129
Updated on Jul 16, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.