Publication:
Dynamic indoor thermal comfort model identification based on neural computing PMV index

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dc.citedby20
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorJalal M.F.A.en_US
dc.contributor.authorHomod R.Z.en_US
dc.contributor.authorEng Y.K.en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid37116431100en_US
dc.contributor.authorid36994633500en_US
dc.contributor.authorid55812454700en_US
dc.date.accessioned2023-12-29T07:45:35Z
dc.date.available2023-12-29T07:45:35Z
dc.date.issued2013
dc.description.abstractThis paper focuses on modelling and simulation of building dynamic thermal comfort control for non-linear HVAC system. Thermal comfort in general refers to temperature and also humidity. However in reality, temperature or humidity is just one of the factors affecting the thermal comfort but not the main measures. Besides, as HVAC control system has the characteristic of time delay, large inertia, and highly nonlinear behaviour, it is difficult to determine the thermal comfort sensation accurately if we use traditional Fanger's PMV index. Hence, Artificial Neural Network (ANN) has been introduced due to its ability to approximate any nonlinear mapping. Using ANN to train, we can get the input-output mapping of HVAC control system or in other word; we can propose a practical approach to identify thermal comfort of a building. Simulations were carried out to validate and verify the proposed method. Results show that the proposed ANN method can track down the desired thermal sensation for a specified condition space. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12113
dc.identifier.doi10.1088/1755-1315/16/1/012113
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84881101851
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84881101851&doi=10.1088%2f1755-1315%2f16%2f1%2f012113&partnerID=40&md5=9fd9c3f3944e84b2c0581da697c29ffc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30216
dc.identifier.volume16
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.subjectPapaya mosaic virus
dc.subjectDelay control systems
dc.subjectNeural networks
dc.subjectThermal comfort
dc.subjectComfort sensations
dc.subjectIndoor thermal comfort
dc.subjectInput-output mapping
dc.subjectModelling and simulations
dc.subjectNonlinear behaviours
dc.subjectNonlinear mappings
dc.subjectThermal comfort control
dc.subjectThermal sensations
dc.subjectair conditioning
dc.subjectartificial neural network
dc.subjectbuilding
dc.subjectcontrol system
dc.subjectheating
dc.subjecthumidity
dc.subjectindex method
dc.subjectnumerical model
dc.subjecttemperature profile
dc.subjectventilation
dc.subjectSensory perception
dc.titleDynamic indoor thermal comfort model identification based on neural computing PMV indexen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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