Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140141
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dc.contributor.authorRadhakrishnan, Nikithaen_US
dc.contributor.authorSrinivasan, Seshadhrien_US
dc.contributor.authorSu, Rongen_US
dc.contributor.authorPoolla, Kameshwaren_US
dc.date.accessioned2020-05-27T01:50:13Z-
dc.date.available2020-05-27T01:50:13Z-
dc.date.issued2017-
dc.identifier.citationRadhakrishnan, N., Srinivasan, S., Su, R., & Poolla, K. (2018). Learning-based hierarchical distributed HVAC scheduling with operational constraints. IEEE Transactions on Control Systems Technology, 26(5), 1892-1900. doi:10.1109/TCST.2017.2728004en_US
dc.identifier.issn1063-6536en_US
dc.identifier.urihttps://hdl.handle.net/10356/140141-
dc.description.abstractThis investigation proposes an energy management system for large multizone commercial buildings that combines distributed optimization with the adaptive learning. While the distributed optimization provides scalability and models the fresh-air infusion as ventilation constraints, the learning algorithm simultaneously captures the influences of occupancy and user interactions. The approach employs a hierarchical architecture and uses a service-oriented framework to propose a distributed optimization method for commercial buildings. In addition, it also includes operational constraints required for optimizing the building energy consumption not studied in the literature. We show that our hierarchical architecture provides much better scalability and minimal performance loss comparable to the centralized approach. We illustrate that the influences of operational constraints on chiller, duct, damper, and ventilation are important for studying the energy savings. The energy saving potential of the proposed approach is illustrated on a 10-zone building, while its scalability is shown via simulations on a 500-zone building. To study the robustness of the approach meeting cancellations or other events that influence zone thermal dynamics, the resulting energy savings are studied. The results demonstrate the advantages of the proposed algorithm in terms of scalability, energy consumption, and robustness.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Control Systems Technologyen_US
dc.rights© 2017 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleLearning-based hierarchical distributed HVAC scheduling with operational constraintsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/TCST.2017.2728004-
dc.identifier.scopus2-s2.0-85029178528-
dc.identifier.issue5en_US
dc.identifier.volume26en_US
dc.identifier.spage1892en_US
dc.identifier.epage1900en_US
dc.subject.keywordsCommercial Buildingen_US
dc.subject.keywordsHeating, Ventilation, and Air-conditioning (HVAC) Systemen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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