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

In 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 nanomateria...

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Main Authors: Ghalandari M., Mukhtar A., Yasir A.S.H.M., Alkhabbaz A., Alviz-Meza A., C�rdenas-Escrocia Y., Le B.N.
Other Authors: 57210118858
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Published: Elsevier Ltd 2024
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spelling my.uniten.dspace-338662024-10-14T11:17:21Z Thermal conductivity improvement in a green building with Nano insulations using machine learning methods Ghalandari M. Mukhtar A. Yasir A.S.H.M. Alkhabbaz A. Alviz-Meza A. C�rdenas-Escrocia Y. Le B.N. 57210118858 57195426549 58518504200 57219669468 57220922265 57194679418 57972795900 Energy saving Green buildings Green house gases Machine learning Nano insulation Optimization Buildings Decision trees Energy dissipation Energy efficiency Energy utilization Learning systems Support vector machines Thermal insulation Conductivity improvement Different thickness Energy savings Energy-savings Green buildings Green house gas Machine learning models Machine-learning Nano insulation Optimisations Greenhouse gases In 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 Authors Final 2024-10-14T03:17:21Z 2024-10-14T03:17:21Z 2023 Article 10.1016/j.egyr.2023.03.123 2-s2.0-85151661321 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151661321&doi=10.1016%2fj.egyr.2023.03.123&partnerID=40&md5=caa6c4c9d934eea9d6b76e0e0b9df9a9 https://irepository.uniten.edu.my/handle/123456789/33866 9 4781 4788 All Open Access Gold Open Access Green Open Access Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Energy saving
Green buildings
Green house gases
Machine learning
Nano insulation
Optimization
Buildings
Decision trees
Energy dissipation
Energy efficiency
Energy utilization
Learning systems
Support vector machines
Thermal insulation
Conductivity improvement
Different thickness
Energy savings
Energy-savings
Green buildings
Green house gas
Machine learning models
Machine-learning
Nano insulation
Optimisations
Greenhouse gases
spellingShingle Energy saving
Green buildings
Green house gases
Machine learning
Nano insulation
Optimization
Buildings
Decision trees
Energy dissipation
Energy efficiency
Energy utilization
Learning systems
Support vector machines
Thermal insulation
Conductivity improvement
Different thickness
Energy savings
Energy-savings
Green buildings
Green house gas
Machine learning models
Machine-learning
Nano insulation
Optimisations
Greenhouse gases
Ghalandari M.
Mukhtar A.
Yasir A.S.H.M.
Alkhabbaz A.
Alviz-Meza A.
C�rdenas-Escrocia Y.
Le B.N.
Thermal conductivity improvement in a green building with Nano insulations using machine learning methods
description In 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 Authors
author2 57210118858
author_facet 57210118858
Ghalandari M.
Mukhtar A.
Yasir A.S.H.M.
Alkhabbaz A.
Alviz-Meza A.
C�rdenas-Escrocia Y.
Le B.N.
format Article
author Ghalandari M.
Mukhtar A.
Yasir A.S.H.M.
Alkhabbaz A.
Alviz-Meza A.
C�rdenas-Escrocia Y.
Le B.N.
author_sort Ghalandari M.
title Thermal conductivity improvement in a green building with Nano insulations using machine learning methods
title_short Thermal conductivity improvement in a green building with Nano insulations using machine learning methods
title_full Thermal conductivity improvement in a green building with Nano insulations using machine learning methods
title_fullStr Thermal conductivity improvement in a green building with Nano insulations using machine learning methods
title_full_unstemmed Thermal conductivity improvement in a green building with Nano insulations using machine learning methods
title_sort thermal conductivity improvement in a green building with nano insulations using machine learning methods
publisher Elsevier Ltd
publishDate 2024
_version_ 1814061091450257408
score 13.209306