Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151121
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dc.contributor.authorChaudhuri, Tanayaen_US
dc.contributor.authorSoh, Yeng Chaien_US
dc.contributor.authorLi, Huaen_US
dc.contributor.authorXie, Lihuaen_US
dc.date.accessioned2021-06-24T10:10:30Z-
dc.date.available2021-06-24T10:10:30Z-
dc.date.issued2019-
dc.identifier.citationChaudhuri, T., Soh, Y. C., Li, H. & Xie, L. (2019). A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings. Applied Energy, 248, 44-53. https://dx.doi.org/10.1016/j.apenergy.2019.04.065en_US
dc.identifier.issn0306-2619en_US
dc.identifier.other0000-0003-3520-4006-
dc.identifier.other0000-0003-4899-9477-
dc.identifier.urihttps://hdl.handle.net/10356/151121-
dc.description.abstractBuilding air-conditioning and mechanical ventilation (ACMV) systems are responsible for significant energy consumption and yet, dissatisfaction with the thermal environment is prevalent among the occupants, revealing a widespread disparity between energy-efficiency and indoor thermal-comfort in buildings. This paper presents an indoor-climate control framework that bridges this gap between energy and comfort. The framework comprises two main components: a thermal-comfort prediction model, and an optimization algorithm termed as the optimal air temperature (OAT) algorithm; they collectively act as an intelligent mediator between the occupant and the ACMV system. Firstly, the ACMV energy consumption is modelled as a function of air temperature, and three operating frequencies of cooling components using a feedforward neural network. Secondly, the thermal-comfort prediction model predicts the thermal state index (TSI: Cool-Discomfort/Comfort/Warm-Discomfort). Thirdly, depending on the predicted TSI, the OAT algorithm locates the optimal operating state such that Comfort state is achieved using the minimum ACMV energy consumption. Proposed framework exhibits an energy saving potential of 36.5%. It is found that 25 °C is the ideal air temperature for desired comfort with minimum energy expense in the tropical buildings. Additionally, six different TSI predictive models including two general and four personal comfort models are implemented to validate the framework. The study is substantiated with extensive real human experiments in controlled thermal environment. The proposed method is scalable for its applicability with any comfort-prediction model, and adaptive for its data-driven architecture. It exhibits the potential to achieve both occupant-comfort and energy-saving through integration with the Internet-of-Things for realizing comfort-energy balanced buildings.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF2011en_US
dc.relationNRFCRP001-090en_US
dc.relation.ispartofApplied Energyen_US
dc.rights© 2019 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleA feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildingsen_US
dc.typeJournal Articleen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en_US
dc.identifier.doi10.1016/j.apenergy.2019.04.065-
dc.identifier.scopus2-s2.0-85064447713-
dc.identifier.volume248en_US
dc.identifier.spage44en_US
dc.identifier.epage53en_US
dc.subject.keywordsIndoor Climate Controlen_US
dc.subject.keywordsThermal Comforten_US
dc.description.acknowledgementThe authors would like to express their sincere appreciation to the CREATE Cambridge Center for Energy Efficiency in Singapore (CARES) and the Center of EXQUISITUS for providing the experiment room and the experimental air-conditioning mechanical ventilation systems in the thermal laboratory of Nanyang Technological University, Singapore. The authors also thank Dr. Deqing Zhai for providing the energy data. This research is jointly supported by the Republic of Singapore’s National Research Foundation under Grant NRF2011 NRFCRP001-090 and the Energy Research Institute at NTU (ERI@N).en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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