Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150840
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dc.contributor.authorZhang, Weien_US
dc.contributor.authorHu, Weizhengen_US
dc.contributor.authorWen, Yonggangen_US
dc.date.accessioned2021-06-02T04:18:16Z-
dc.date.available2021-06-02T04:18:16Z-
dc.date.issued2019-
dc.identifier.citationZhang, W., Hu, W. & Wen, Y. (2019). Thermal comfort modeling for smart buildings : a fine-grained deep learning approach. IEEE Internet of Things Journal, 6(2), 2540-2549. https://dx.doi.org/10.1109/JIOT.2018.2871461en_US
dc.identifier.issn2327-4662en_US
dc.identifier.other0000-0002-2644-2582-
dc.identifier.other0000-0001-8347-3433-
dc.identifier.other0000-0002-2751-5114-
dc.identifier.urihttps://hdl.handle.net/10356/150840-
dc.description.abstractThe emerging Internet of Things (IoT) technology enables smart building management and operation to improve building energy efficiency and occupant thermal comfort. In this paper, we perform data analysis using the IoT generated building data to derive accurate thermal comfort model for smart building control. Deep neural network (DNN) is used to model the relationship between the controllable building operations and thermal comfort. As thermal comfort is determined by multiple comfort factors, a fine-grained architecture is proposed, where an exclusive model is trained for each factor and accordingly the corresponding thermal comfort can be evaluated. The experimental results show that the proposed fine-grained DNN outperforms its coarse-grained counterpart by 3.5× and is 1.7×, 2.5×, 2.4×, and 1.9× more accurate compared to four popular machine learning algorithms. Besides, DNN's performance promotes with deeper network topology and more neurons, and a simple topology with the same number of neurons per network hidden layer is sufficient to achieve high modeling accuracy. Finally, the derived thermal comfort model reveals a linear relationship between comfort and air conditioning setpoint. The linear property helps quickly and accurately search for the optimal controllable setpoint with the desired comfort.en_US
dc.description.sponsorshipBuilding and Construction Authority (BCA)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF2015ENC-GBICRD001-012en_US
dc.relation.ispartofIEEE Internet of Things Journalen_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JIOT.2018.2871461.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleThermal comfort modeling for smart buildings : a fine-grained deep learning approachen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/JIOT.2018.2871461-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85053614713-
dc.identifier.issue2en_US
dc.identifier.volume6en_US
dc.identifier.spage2540en_US
dc.identifier.epage2549en_US
dc.subject.keywordsInternet of Thingsen_US
dc.subject.keywordsSmart Buildingsen_US
dc.subject.keywordsGreen Buildingen_US
dc.description.acknowledgementThis work was supported by the Singapore National Research Foundation (NRF) via the Green Buildings Innovation Cluster (GBIC) administered by the Building and Construction Authority under Grant NRF2015ENC-GBICRD001-012.en_US
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