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Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

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dc.citedby15
dc.contributor.authorGhalandari M.en_US
dc.contributor.authorMukhtar A.en_US
dc.contributor.authorYasir A.S.H.M.en_US
dc.contributor.authorAlkhabbaz A.en_US
dc.contributor.authorAlviz-Meza A.en_US
dc.contributor.authorC�rdenas-Escrocia Y.en_US
dc.contributor.authorLe B.N.en_US
dc.contributor.authorid57210118858en_US
dc.contributor.authorid57195426549en_US
dc.contributor.authorid58518504200en_US
dc.contributor.authorid57219669468en_US
dc.contributor.authorid57220922265en_US
dc.contributor.authorid57194679418en_US
dc.contributor.authorid57972795900en_US
dc.date.accessioned2024-10-14T03:17:21Z
dc.date.available2024-10-14T03:17:21Z
dc.date.issued2023
dc.description.abstractIn this paper, the energy loss of the green building is optimized based on the thickness and lay-up of the Nano-insulation. As different thicknesses and lay-up of the Nano-insulation have a direct effect on energy consumption of the green building with 1590 square meters, especially with nanomaterial, the machine learning models are employed to represent a new model of the thermal conductivity of the proposed advanced insulation with the precision above 99%. The machine learning models are employed to classify and model the behavior of the heat transfer in the green building due to the complex behavior of the thermal conductivity in the green building. Therefore, 110 data for modeling 20 types of lay-up with 6 different thicknesses are prepared by the machine learning models including Support Vector Machine (SVM), Gaussian Process Regression (GPR), and decision tree. Based on the data analysis and statistical data, thermal conductivity modeling with a decision tree represents the best performance and fitted model. The multi-Disciplinary Optimizing method (MDO) under energy consumption constraint, economical consideration, and environmental effects on insulation properties is performed to enhance the energy efficiency of the green building. The calculated results with the Degree-Day approach reveal that the amount of energy saving for green buildings with Nano insulation is about 40% higher than common insulation in common types of insulations. The proposed insulation characteristics regarding the value of Present Worth Function (PWF) and economic aspects cause energy saving per unit area and decreasing in CO2 emission between 290 kg/m3 to 293 kg/m3 depending on weather conditions, insulation thickness, and lay-up. � 2023 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.egyr.2023.03.123
dc.identifier.epage4788
dc.identifier.scopus2-s2.0-85151661321
dc.identifier.spage4781
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85151661321&doi=10.1016%2fj.egyr.2023.03.123&partnerID=40&md5=caa6c4c9d934eea9d6b76e0e0b9df9a9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33866
dc.identifier.volume9
dc.pagecount7
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleEnergy Reports
dc.subjectEnergy saving
dc.subjectGreen buildings
dc.subjectGreen house gases
dc.subjectMachine learning
dc.subjectNano insulation
dc.subjectOptimization
dc.subjectBuildings
dc.subjectDecision trees
dc.subjectEnergy dissipation
dc.subjectEnergy efficiency
dc.subjectEnergy utilization
dc.subjectLearning systems
dc.subjectSupport vector machines
dc.subjectThermal insulation
dc.subjectConductivity improvement
dc.subjectDifferent thickness
dc.subjectEnergy savings
dc.subjectEnergy-savings
dc.subjectGreen buildings
dc.subjectGreen house gas
dc.subjectMachine learning models
dc.subjectMachine-learning
dc.subjectNano insulation
dc.subjectOptimisations
dc.subjectGreenhouse gases
dc.titleThermal conductivity improvement in a green building with Nano insulations using machine learning methodsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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