Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150158
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dc.contributor.authorHu, Weizhengen_US
dc.contributor.authorLuo, Yongen_US
dc.contributor.authorLu, Zongqingen_US
dc.contributor.authorWen, Yonggangen_US
dc.date.accessioned2021-05-19T03:36:03Z-
dc.date.available2021-05-19T03:36:03Z-
dc.date.issued2019-
dc.identifier.citationHu, W., Luo, Y., Lu, Z. & Wen, Y. (2019). Heterogeneous transfer learning for thermal comfort modeling. 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys 2019), 61-70. https://dx.doi.org/10.1145/3360322.3360843en_US
dc.identifier.isbn9781450370059-
dc.identifier.urihttps://hdl.handle.net/10356/150158-
dc.description.abstractFor decades, the Predicted Mean Vote (PMV) model has been adopted to evaluate building occupants' thermal comfort. However, recent studies argue that the PMV model is inaccurate and suffers from two major issues: thermal comfort parameter inadequacy and modeling data inadequacy. To overcome these issues, in this paper, we propose a learning-based approach for thermal comfort modeling, named as Heterogeneous Transfer Learning (HTL) based Intelligent Thermal Comfort Neural Network (HTL-ITCNN). First, to address the parameter inadequacy issue, we add more relevant factors as the modeling features except for the six PMV parameters. Due to the flexibility of learning-based approaches, newly found thermal comfort parameters can be appended to extend the number of modeling features. Second, to mitigate the impact of the data inadequacy issue, we adopt the deep transfer learning techniques to train the thermal comfort model, where the model training would benefit from the transferred knowledge from the existing datasets. Due to the heterogeneity of the features among different datasets, we follow the HTL concept to conducting effective knowledge transfer among heterogeneous domains, which are the different but related datasets with varied features. To validate our solution, we conduct five-month data collection experiments and build our datasets. With the HTL-based two-stage learning paradigm, the experimental results show that the accuracy of HTL-ITCNN outperforms the PMV model by on average 73.9%. Besides, we verify the impacts of newly added features and knowledge transfer on model performance. Moreover, we demonstrate the enormous potential of personal thermal comfort modeling research.en_US
dc.description.sponsorshipBuilding and Construction Authority (BCA)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF2015ENC-GBICRD001-012en_US
dc.relationDSAIR@NTUen_US
dc.relationBSEWWT2017_2_06en_US
dc.relationSinBerBEST-2012-01en_US
dc.rights© 2019 Association for Computing Machinery. All rights reserved. This paper was published in 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys 2019) and is made available with permission of Association for Computing Machinery.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleHeterogeneous transfer learning for thermal comfort modelingen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conference6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys 2019)en_US
dc.identifier.doi10.1145/3360322.3360843-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85077264730-
dc.identifier.spage61en_US
dc.identifier.epage70en_US
dc.subject.keywordsThermal Comfort Modelingen_US
dc.subject.keywordsPervasive Sensingen_US
dc.citation.conferencelocationNew York, USAen_US
dc.description.acknowledgementThis research is funded by National Research Foundation (NRF) via the Green Buildings Innovation Cluster (Grant NO.: NRF2015ENC￾GBICRD001-012), administered by Building and Construction Au￾thority (BCA) Singapore. In addition, this research is sponsored by National Research Foundation (NRF) via the Behavioural Stud￾ies in Energy, Water, Waste and Transportation Sectors (Grant NO.: BSEWWT2017_2_06), administered by National University of Singapore (NUS). Moreover, this research is funded by Nanyang Technological University (NTU) via the Data Science & Artificial Intelligence Research Centre @ NTU (Grant NO.: DSAIR@NTU). Furthermore, this research is supported by the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program (Grant NO.: SinBerBEST-2012-01). BEARS has been es￾tablished by the University of California, Berkeley as a center for intellectual excellence in research and education in Singapore.en_US
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