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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723003608 |
| _version_ | 1797781839411150848 |
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| author | Mohammad Ghalandari Azfarizal Mukhtar Ahmad Shah Hizam Md Yasir Ali Alkhabbaz Aníbal Alviz-Meza Yulineth Cárdenas-Escrocia Binh Nguyen Le |
| author_facet | Mohammad Ghalandari Azfarizal Mukhtar Ahmad Shah Hizam Md Yasir Ali Alkhabbaz Aníbal Alviz-Meza Yulineth Cárdenas-Escrocia Binh Nguyen Le |
| author_sort | Mohammad Ghalandari |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-13T00:02:37Z |
| format | Article |
| id | doaj.art-247995018ccb49af8884cc71bf4fbd7d |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T00:02:37Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-247995018ccb49af8884cc71bf4fbd7d2023-07-13T05:29:56ZengElsevierEnergy Reports2352-48472023-12-01947814788Thermal conductivity improvement in a green building with Nano insulations using machine learning methodsMohammad Ghalandari0Azfarizal Mukhtar1Ahmad Shah Hizam Md Yasir2Ali Alkhabbaz3Aníbal Alviz-Meza4Yulineth Cárdenas-Escrocia5Binh Nguyen Le6Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam; School of Engineering & Technology, Duy Tan University, Da Nang, Viet NamInstitute of Sustainable Energy, Putrajaya Campus, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, MalaysiaFaculty of Resilience, Rabdan Academy, 65, Al Inshirah, Al Sa’adah, Abu Dhabi, 22401, PO Box: 114646, Abu Dhabi, United Arab EmiratesDepartment of Aeronautical Technical Engineering, Technical College, Al- Kitab University, Kirkuk, Iraq; Department of Mechanical Engineering, College of Engineering, University of Mosul, Mosul, IraqGrupo de Investigación en Deterioro de Materiales, Transición Energética y Ciencia de datos DANT3, Universidad Señor de Sipán, Facultad de Ingenieria y Urbanismo, Km 5 Via Pimentel, Chiclayo, 14001, Peru; Department of Energy, Universidad de la Costa, Barranquilla, Colombia; Corresponding author at: Department of Mechanical Engineering, College of Engineering, University of Mosul, Mosul, Iraq.Department of Energy, Universidad de la Costa, Barranquilla, ColombiaInstitute of Research and Development, Duy Tan University, Da Nang, Viet Nam; School of Engineering & Technology, Duy Tan University, Da Nang, Viet NamIn 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.http://www.sciencedirect.com/science/article/pii/S2352484723003608Machine learningOptimizationNano insulationGreen house gasesEnergy savingGreen buildings |
| spellingShingle | Mohammad Ghalandari Azfarizal Mukhtar Ahmad Shah Hizam Md Yasir Ali Alkhabbaz Aníbal Alviz-Meza Yulineth Cárdenas-Escrocia Binh Nguyen Le Thermal conductivity improvement in a green building with Nano insulations using machine learning methods Energy Reports Machine learning Optimization Nano insulation Green house gases Energy saving Green buildings |
| title | 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_short | 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 |
| topic | Machine learning Optimization Nano insulation Green house gases Energy saving Green buildings |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723003608 |
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