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...
- Autores:
-
Ghalandari, Mohammad
Mukhtar, Azfarizal
Hizam Md Yasir, Ahmad Shah
Alkhabbaz, Ali
Alviz Meza, Anibal
Cardenas Escorcia, Yulineth
Binh, Le Nguyen
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10496
- Acceso en línea:
- https://hdl.handle.net/11323/10496
https://repositorio.cuc.edu.co/
- Palabra clave:
- Machine learning
Optimization
Nano insulation
Green house gases
Energy saving
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Thermal conductivity improvement in a green building with Nano insulations using machine learning methods |
| title |
Thermal conductivity improvement in a green building with Nano insulations using machine learning methods |
| spellingShingle |
Thermal conductivity improvement in a green building with Nano insulations using machine learning methods Machine learning Optimization Nano insulation Green house gases Energy saving |
| 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 |
| dc.creator.fl_str_mv |
Ghalandari, Mohammad Mukhtar, Azfarizal Hizam Md Yasir, Ahmad Shah Alkhabbaz, Ali Alviz Meza, Anibal Cardenas Escorcia, Yulineth Binh, Le Nguyen |
| dc.contributor.author.none.fl_str_mv |
Ghalandari, Mohammad Mukhtar, Azfarizal Hizam Md Yasir, Ahmad Shah Alkhabbaz, Ali Alviz Meza, Anibal Cardenas Escorcia, Yulineth Binh, Le Nguyen |
| dc.subject.proposal.eng.fl_str_mv |
Machine learning Optimization Nano insulation Green house gases Energy saving |
| topic |
Machine learning Optimization Nano insulation Green house gases Energy saving |
| 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. |
| publishDate |
2023 |
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2023-09-18T16:18:16Z |
| dc.date.available.none.fl_str_mv |
2023-09-18T16:18:16Z |
| dc.date.issued.none.fl_str_mv |
2023 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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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, Volume 9, 2023, Pages 4781-4788, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2023.03.123. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/10496 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.egyr.2023.03.123 |
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2352-4847 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
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, Volume 9, 2023, Pages 4781-4788, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2023.03.123. 10.1016/j.egyr.2023.03.123 2352-4847 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/10496 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Energy Reports |
| dc.relation.references.spa.fl_str_mv |
Abdou, N., Mghouchi, Y.E.L., Hamdaoui, S., Asri, N.E.L., Mouqallid, M., 2021. Multiobjective optimization of passive energy efficiency measures for net-zero energy building in Morocco. Build Environ 204, 108141. Ahmad, T., Chen, H., Huang, R., Yabin, G., Wang, J., Shair, J., et al., 2018. Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment. Energy 158, 17–32. Ahmadi, M.H., Ghazvini, M., Sadeghzadeh, M., Nazari, M.A., Ghalandari, M., 2019. Utilization of hybrid nanofluids in solar energy applications: a review. Nano-Structures Nano-Objects 20, 100386. Akan, A.P., Akan, A.E., 2022. Modeling of CO2 emissions via optimum insulation thickness of residential buildings. Clean Technol Environ Policy 24, 949–967. Akan, A.E., Ünal, F., Koçyiğit, F., 2021. Investigation of energy saving potential in buildings using novel developed lightweight concrete. Int J Thermophys 42, 1–28. Aktemur, C., Bilgin, F., Tunçkol, S., 2021. Optimisation on the thermal insulation layer thickness in buildings with environmental analysis: an updated comprehensive study for Turkey’s all provinces. J Therm Eng 7, 1239–1256. Akyüz, M.K., 2021. Determining economic and environmental impact of insulation by thermoeconomic and life cycle assessment analysis for different climate regions of Turkey. Energy Sources, Part A Recover Util Environ Eff 43, 829–851. Alfarawi, S., Omar, H., El-Sawi, A., Al Jubori, A., 2022;. Thermal performance assessment of external wall construction for energy-efficient buildings. Eur J Sustain Dev Res 6, em0189. Alsurakji, I., Abdallah, R., Assad, M., El-Qanni, A., 2021. Energy savings and optimum insulation thickness in external walls in Palestinian buildings. In: 2021 12th Int. Renew. Eng. Conf. pp. 1–5. Altun, A.F., 2022. Determination of optimum building envelope parameters of a room concerning window-to-wall ratio, orientation, insulation thickness and window type. Buildings 12, 383. Arumugam, C., Shaik, S., 2021. Transforming waste disposals into building materials to investigate energy savings and carbon emission mitigation potential. Environ. Sci. Pollut. Res. 28, 15259–15273. Aydin, N., Biyikoğlu, A., 2021. Determination of optimum insulation thickness by life cycle cost analysis for residential buildings in Turkey. Sci Technol Built Environ 27, 2–13. Azmi, N.A., Arıcı, M., Baharun, A., 2021. A review on the factors influencing energy efficiency of mosque buildings. J Clean Prod 292, 126010. Bagheri-Esfeh, H., Dehghan, M.R., 2022. Multi-objective optimization of setpoint temperature of thermostats in residential buildings. Energy Build 261, 111955. Bataineh, K., Al Rabee, A., 2022. Design optimization of energy efficient residential buildings in mediterranean region. J Sustain Dev Energy, Water Environ Syst 10, 1–21. Charbuty, B., Abdulazeez, A., 2021. Classification based on decision tree algorithm for machine learning. J Appl Sci Technol Trends 2, 20–28. Chersoni, G., DellaValle, N., Fontana, M., 2022. Modelling thermal insulation investment choice in the EU via a behaviourally informed agent-based model. Energy Policy 163, 112823. Duman, Ö, Koca, A., Acet, R.C., Çetin, M.G., Gemici, Z., 2015. A study on optimum insulation thickness in walls and energy savings based on degree day approach for 3 different demo-sites in europe. In: Proc. Int. Conf. CISBAT 2015 Futur. Build. Dist. Sustain. from Nano To Urban Scale. pp. 155–160. Ertürk, M., Keçebaş, A., 2021. Prediction of the effect of insulation thickness and emission on heating energy requirements of cities in the future. Sustain Cities Soc 75, 103270. Fathi, S., Srinivasan, R., Fenner, A., Fathi, S., 2020. Machine learning applications in urban building energy performance forecasting: A systematic review. Renew Sustain Energy Rev 133, 110287. Felius, L.C., Dessen, F., Hrynyszyn, B.D., 2020. Retrofitting towards energyefficient homes in European cold climates: a review. Energy Effic 13, 101–125. Geng, Y., Han, X., Zhang, H., Shi, L., 2021. Optimization and cost analysis of thickness of vacuum insulation panel for structural insulating panel buildings in cold climates. J Build Eng 33, 101853. Ghalandari, M., Mahariq, I., Ghadak, F., Accouche, O., Jarad, F., 2022. Aeroelastic optimization of the high aspect ratio wing with Aileron. C Mater & Contin 70, 5569–5581. Ghalandari, M., Ziamolki, A., Mosavi, A., Shamshirb, S., Chau, K.-W., Bornassi, S., 2019. Aeromechanical optimization of first row compressor test stand blades using a hybrid machine learning model of genetic algorithm, artificial neural networks and design of experiments. Eng Appl Comput Fluid Mech 13, 892–904. Gustavsson, L., Piccardo, C., 2022. Cost optimized building energy retrofit measures and primary energy savings under different retrofitting materials, economic scenarios, and energy supply. Energies 15, 1009. Hasan, A., 1999. Optimizing insulation thickness for buildings using life cycle cost. Appl. Energy 63, 115–124. Heracleous, C., Michael, A., Savvides, A., Hayles, C., 2022. A methodology to assess energy-demand savings and cost-effectiveness of adaptation measures in educational buildings in the warm mediterranean region. Energy Rep. 8, 5472–5486. Hou, J., Zhang, T., Hou, C., Fukuda, H., et al., 2022. A study on influencing factors of optimum insulation thickness of exterior walls for rural traditional dwellings in northeast of Sichuan hills, China. Case Stud Constr Mater 16, e01033. Hu, Y.-J., Huang, H., Wang, H., Li, C., Deng, Y., 2023. Exploring cost-effective strategies for emission reduction of public buildings in a life-cycle. Energy Build 112927. Hu, W., Xia, Y., Li, F., Yu, H., Hou, C., Meng, X., 2021. Effect of the filling position and filling rate of the insulation material on the insulation performance of the hollow block. Case Stud Therm Eng 26, 101023. Huang, J., Wang, S., Teng, F., Feng, W., 2021. Thermal performance optimization of envelope in the energy-saving renovation of existing residential buildings. Energy Build 247, 111103. Kharrufa, S.N., Noori, F., 2022. A review of thermal design for buildings in hot climates. Pertanika J Sci Technol 30. Kon, O., İsmail, Caner, İlten, N., 2021. Life cycle assessment of energy-efficient improvement for external walls of hospital building. Int J Glob Warm 25, 408–424. Koru, M., Korkmaz, E., Kan, M., 2022. Determination of the effect of the change in the thermal conductivity coefficient of EPS depending on the density and temperature on the optimum insulation thickness. Int J Thermophys 43, 1–14. Küçüktopcu, E., Cemek, B., 2021. The use of artificial neural networks to estimate optimum insulation thickness, energy savings, and carbon dioxide emissions. Environ Prog Sustain Energy 40, e13478. Kunelbayev, M., Amirgaliyev, Y., Sundetov, T., 2022. Improving the efficiency of environmental temperature control in homes and buildings. Energies 15, 8839. Li, Q., Ma, L., Li, D., Arıcı, M., Yıldız, Ç., Wang, Z., et al., 2021. Thermoeconomic analysis of a wall incorporating phase change material in a rural residence located in northeast China. Sustain Energy Technol Assess 44, 101091. Manzhos, S., Sasaki, E., Ihara, M., 2022. Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data. Mach Learn Sci Technol 3, 01LT02. Mercan, H., Çelen, A., Taner, T., 2022. Thermophysical and rheological properties of unitary and hybrid nanofluids. Adv. Nanofluid Heat Transf. Elsevier 95–129. Mohebian, R., Riahi, M.A., 2019. Integrating neural, fuzzy logic, and neurofuzzy approaches using ant colony optimisation for continuous domains to determine carbonate reservoir facies. Boll Di Geofis Teor Ed Appl 60. Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirb, S., Varkonyi-Koczy, A.R., 2019. State of the art of machine learning models in energy systems, a systematic review. Energies 12, 1301. Motaghian, S., Saharkhiz, M.H.M., Rayegan, S., Pasdarshahri, H., Ahmadi, P., Rosen, M.A., 2021. Techno-economic multi-objective optimization of detailed external wall insulation scenarios for buildings in moderate-dry regions. Sustain Energy Technol Assess 46, 101256. Ozel, M., 2011. Thermal performance and optimum insulation thickness of building walls with different structure materials. Appl Therm Eng 31, 3854–3863. Ramya, K., Teekaraman, Y., Kumar, K.A.R., 2019. Fuzzy-based energy management system with decision tree algorithm for power security system. Int J Comput Intell Syst 12, 1173–1178. Shaik, S., Gorantla, K., Ghosh, A., Arumugam, C., Maduru, V.R., 2021. Energy savings and carbon emission mitigation prospective of building’s glazing variety, window-to-wall ratio and wall thickness. Energies 14, 8020. Suerdem, K., Taner, T., Acikgoz, O., Dalkilic, A.S., Panchal, H., 2023. Performance of refrigerants employed in rooftop air-conditioners. J Build Eng 106301. Tunçbilek, E., Komerska, A., Arıcı, M., 2022. Optimisation of wall insulation thickness using energy management strategies: Intermittent versus continuous operation schedule. Sustain Energy Technol Assess. 49, 101778. Tushar, Q., Bhuiyan, M., Sandanayake, M., Zhang, G., 2019. Optimizing the energy consumption in a residential building at different climate zones: Towards sustainable decision making. J Clean Prod 233, 634–649. Uludaş, M.Ç.., Tunçbilek, E., Yıldız, Ç., Arıcı, M., Li, D., Krajčík, M., 2022. PCMenhanced sunspace for energy efficiency and CO2 mitigation in a house in mediterranean climate. J Build Eng 57, 104856. Ustaoglu, A., Yaras, A., Sutcu, M., Gencel, O., 2021. Investigation of the residential building having novel environment-friendly construction materials with enhanced energy performance in diverse climate regions: Cost-efficient, low-energy and low-carbon emission. J Build Eng 43, 102617. Wang, L., Zhang, G., Yin, X., Zhang, H., Ghalandari, M., 2022. Optimal control of renewable energy in buildings using the machine learning method. Sustain Energy Technol Assess 53, 102534. Yi, Y., Wang, L., Chen, Z., 2021. Adaptive global kernel interval SVRbased machine learning for accelerated dielectric constant prediction of polymer-based dielectric energy storage. Renew. Energy 176, 81–88. Yüksel, A., Arıcı, M., Krajčík, M., Civan, M., H., Karabay, 2021. A review on thermal comfort, indoor air quality and energy consumption in temples. J Build Eng 35, 102013. Zeng, A., Ho, H., Yu, Y., 2020. Prediction of building electricity usage using Gaussian process regression. J Build Eng 28, 101054. Zhang, G., Ge, Y., Pan, X., Afsharzadeh, M.S., Ghalandari, M., 2022. Optimization of energy consumption of a green building using PSO-SVM algorithm. Sustain Energy Technol Assess 53, 102667. Zhou, G., Zhou, Y., Huang, H., Tang, Z., 2019. Functional networks and applications: A survey. Neurocomputing 335, 384–399. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)/© 2023 The Authors. Published by Elsevier Ltd.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ghalandari, MohammadMukhtar, AzfarizalHizam Md Yasir, Ahmad ShahAlkhabbaz, AliAlviz Meza, AnibalCardenas Escorcia, YulinethBinh, Le Nguyen2023-09-18T16:18:16Z2023-09-18T16:18:16Z2023Mohammad 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, Volume 9, 2023, Pages 4781-4788, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2023.03.123.https://hdl.handle.net/11323/1049610.1016/j.egyr.2023.03.1232352-4847Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.8 páginasapplication/pdfengElsevier Ltd.United Kingdomhttps://www.sciencedirect.com/science/article/pii/S2352484723003608Thermal conductivity improvement in a green building with Nano insulations using machine learning methodsArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Energy ReportsAbdou, N., Mghouchi, Y.E.L., Hamdaoui, S., Asri, N.E.L., Mouqallid, M., 2021. Multiobjective optimization of passive energy efficiency measures for net-zero energy building in Morocco. Build Environ 204, 108141.Ahmad, T., Chen, H., Huang, R., Yabin, G., Wang, J., Shair, J., et al., 2018. Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment. Energy 158, 17–32.Ahmadi, M.H., Ghazvini, M., Sadeghzadeh, M., Nazari, M.A., Ghalandari, M., 2019. Utilization of hybrid nanofluids in solar energy applications: a review. Nano-Structures Nano-Objects 20, 100386.Akan, A.P., Akan, A.E., 2022. Modeling of CO2 emissions via optimum insulation thickness of residential buildings. Clean Technol Environ Policy 24, 949–967.Akan, A.E., Ünal, F., Koçyiğit, F., 2021. Investigation of energy saving potential in buildings using novel developed lightweight concrete. Int J Thermophys 42, 1–28.Aktemur, C., Bilgin, F., Tunçkol, S., 2021. Optimisation on the thermal insulation layer thickness in buildings with environmental analysis: an updated comprehensive study for Turkey’s all provinces. J Therm Eng 7, 1239–1256.Akyüz, M.K., 2021. Determining economic and environmental impact of insulation by thermoeconomic and life cycle assessment analysis for different climate regions of Turkey. Energy Sources, Part A Recover Util Environ Eff 43, 829–851.Alfarawi, S., Omar, H., El-Sawi, A., Al Jubori, A., 2022;. Thermal performance assessment of external wall construction for energy-efficient buildings. Eur J Sustain Dev Res 6, em0189.Alsurakji, I., Abdallah, R., Assad, M., El-Qanni, A., 2021. Energy savings and optimum insulation thickness in external walls in Palestinian buildings. In: 2021 12th Int. Renew. Eng. Conf. pp. 1–5.Altun, A.F., 2022. Determination of optimum building envelope parameters of a room concerning window-to-wall ratio, orientation, insulation thickness and window type. Buildings 12, 383.Arumugam, C., Shaik, S., 2021. Transforming waste disposals into building materials to investigate energy savings and carbon emission mitigation potential. Environ. Sci. Pollut. Res. 28, 15259–15273.Aydin, N., Biyikoğlu, A., 2021. Determination of optimum insulation thickness by life cycle cost analysis for residential buildings in Turkey. Sci Technol Built Environ 27, 2–13.Azmi, N.A., Arıcı, M., Baharun, A., 2021. A review on the factors influencing energy efficiency of mosque buildings. J Clean Prod 292, 126010.Bagheri-Esfeh, H., Dehghan, M.R., 2022. Multi-objective optimization of setpoint temperature of thermostats in residential buildings. Energy Build 261, 111955.Bataineh, K., Al Rabee, A., 2022. Design optimization of energy efficient residential buildings in mediterranean region. J Sustain Dev Energy, Water Environ Syst 10, 1–21.Charbuty, B., Abdulazeez, A., 2021. Classification based on decision tree algorithm for machine learning. J Appl Sci Technol Trends 2, 20–28.Chersoni, G., DellaValle, N., Fontana, M., 2022. Modelling thermal insulation investment choice in the EU via a behaviourally informed agent-based model. Energy Policy 163, 112823.Duman, Ö, Koca, A., Acet, R.C., Çetin, M.G., Gemici, Z., 2015. A study on optimum insulation thickness in walls and energy savings based on degree day approach for 3 different demo-sites in europe. In: Proc. Int. Conf. CISBAT 2015 Futur. Build. Dist. Sustain. from Nano To Urban Scale. pp. 155–160.Ertürk, M., Keçebaş, A., 2021. Prediction of the effect of insulation thickness and emission on heating energy requirements of cities in the future. Sustain Cities Soc 75, 103270.Fathi, S., Srinivasan, R., Fenner, A., Fathi, S., 2020. Machine learning applications in urban building energy performance forecasting: A systematic review. Renew Sustain Energy Rev 133, 110287.Felius, L.C., Dessen, F., Hrynyszyn, B.D., 2020. Retrofitting towards energyefficient homes in European cold climates: a review. Energy Effic 13, 101–125.Geng, Y., Han, X., Zhang, H., Shi, L., 2021. Optimization and cost analysis of thickness of vacuum insulation panel for structural insulating panel buildings in cold climates. J Build Eng 33, 101853.Ghalandari, M., Mahariq, I., Ghadak, F., Accouche, O., Jarad, F., 2022. Aeroelastic optimization of the high aspect ratio wing with Aileron. C Mater & Contin 70, 5569–5581.Ghalandari, M., Ziamolki, A., Mosavi, A., Shamshirb, S., Chau, K.-W., Bornassi, S., 2019. Aeromechanical optimization of first row compressor test stand blades using a hybrid machine learning model of genetic algorithm, artificial neural networks and design of experiments. Eng Appl Comput Fluid Mech 13, 892–904.Gustavsson, L., Piccardo, C., 2022. Cost optimized building energy retrofit measures and primary energy savings under different retrofitting materials, economic scenarios, and energy supply. Energies 15, 1009.Hasan, A., 1999. Optimizing insulation thickness for buildings using life cycle cost. Appl. Energy 63, 115–124.Heracleous, C., Michael, A., Savvides, A., Hayles, C., 2022. A methodology to assess energy-demand savings and cost-effectiveness of adaptation measures in educational buildings in the warm mediterranean region. Energy Rep. 8, 5472–5486.Hou, J., Zhang, T., Hou, C., Fukuda, H., et al., 2022. A study on influencing factors of optimum insulation thickness of exterior walls for rural traditional dwellings in northeast of Sichuan hills, China. Case Stud Constr Mater 16, e01033.Hu, Y.-J., Huang, H., Wang, H., Li, C., Deng, Y., 2023. Exploring cost-effective strategies for emission reduction of public buildings in a life-cycle. Energy Build 112927.Hu, W., Xia, Y., Li, F., Yu, H., Hou, C., Meng, X., 2021. Effect of the filling position and filling rate of the insulation material on the insulation performance of the hollow block. Case Stud Therm Eng 26, 101023.Huang, J., Wang, S., Teng, F., Feng, W., 2021. Thermal performance optimization of envelope in the energy-saving renovation of existing residential buildings. Energy Build 247, 111103.Kharrufa, S.N., Noori, F., 2022. A review of thermal design for buildings in hot climates. Pertanika J Sci Technol 30.Kon, O., İsmail, Caner, İlten, N., 2021. Life cycle assessment of energy-efficient improvement for external walls of hospital building. Int J Glob Warm 25, 408–424.Koru, M., Korkmaz, E., Kan, M., 2022. Determination of the effect of the change in the thermal conductivity coefficient of EPS depending on the density and temperature on the optimum insulation thickness. Int J Thermophys 43, 1–14.Küçüktopcu, E., Cemek, B., 2021. The use of artificial neural networks to estimate optimum insulation thickness, energy savings, and carbon dioxide emissions. Environ Prog Sustain Energy 40, e13478.Kunelbayev, M., Amirgaliyev, Y., Sundetov, T., 2022. Improving the efficiency of environmental temperature control in homes and buildings. Energies 15, 8839.Li, Q., Ma, L., Li, D., Arıcı, M., Yıldız, Ç., Wang, Z., et al., 2021. Thermoeconomic analysis of a wall incorporating phase change material in a rural residence located in northeast China. Sustain Energy Technol Assess 44, 101091.Manzhos, S., Sasaki, E., Ihara, M., 2022. Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data. Mach Learn Sci Technol 3, 01LT02.Mercan, H., Çelen, A., Taner, T., 2022. Thermophysical and rheological properties of unitary and hybrid nanofluids. Adv. Nanofluid Heat Transf. Elsevier 95–129.Mohebian, R., Riahi, M.A., 2019. Integrating neural, fuzzy logic, and neurofuzzy approaches using ant colony optimisation for continuous domains to determine carbonate reservoir facies. Boll Di Geofis Teor Ed Appl 60.Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirb, S., Varkonyi-Koczy, A.R., 2019. State of the art of machine learning models in energy systems, a systematic review. Energies 12, 1301.Motaghian, S., Saharkhiz, M.H.M., Rayegan, S., Pasdarshahri, H., Ahmadi, P., Rosen, M.A., 2021. Techno-economic multi-objective optimization of detailed external wall insulation scenarios for buildings in moderate-dry regions. Sustain Energy Technol Assess 46, 101256.Ozel, M., 2011. Thermal performance and optimum insulation thickness of building walls with different structure materials. Appl Therm Eng 31, 3854–3863.Ramya, K., Teekaraman, Y., Kumar, K.A.R., 2019. Fuzzy-based energy management system with decision tree algorithm for power security system. Int J Comput Intell Syst 12, 1173–1178.Shaik, S., Gorantla, K., Ghosh, A., Arumugam, C., Maduru, V.R., 2021. Energy savings and carbon emission mitigation prospective of building’s glazing variety, window-to-wall ratio and wall thickness. Energies 14, 8020.Suerdem, K., Taner, T., Acikgoz, O., Dalkilic, A.S., Panchal, H., 2023. Performance of refrigerants employed in rooftop air-conditioners. J Build Eng 106301.Tunçbilek, E., Komerska, A., Arıcı, M., 2022. Optimisation of wall insulation thickness using energy management strategies: Intermittent versus continuous operation schedule. Sustain Energy Technol Assess. 49, 101778.Tushar, Q., Bhuiyan, M., Sandanayake, M., Zhang, G., 2019. Optimizing the energy consumption in a residential building at different climate zones: Towards sustainable decision making. J Clean Prod 233, 634–649.Uludaş, M.Ç.., Tunçbilek, E., Yıldız, Ç., Arıcı, M., Li, D., Krajčík, M., 2022. PCMenhanced sunspace for energy efficiency and CO2 mitigation in a house in mediterranean climate. J Build Eng 57, 104856.Ustaoglu, A., Yaras, A., Sutcu, M., Gencel, O., 2021. Investigation of the residential building having novel environment-friendly construction materials with enhanced energy performance in diverse climate regions: Cost-efficient, low-energy and low-carbon emission. J Build Eng 43, 102617.Wang, L., Zhang, G., Yin, X., Zhang, H., Ghalandari, M., 2022. Optimal control of renewable energy in buildings using the machine learning method. Sustain Energy Technol Assess 53, 102534.Yi, Y., Wang, L., Chen, Z., 2021. Adaptive global kernel interval SVRbased machine learning for accelerated dielectric constant prediction of polymer-based dielectric energy storage. Renew. Energy 176, 81–88.Yüksel, A., Arıcı, M., Krajčík, M., Civan, M., H., Karabay, 2021. A review on thermal comfort, indoor air quality and energy consumption in temples. J Build Eng 35, 102013.Zeng, A., Ho, H., Yu, Y., 2020. Prediction of building electricity usage using Gaussian process regression. J Build Eng 28, 101054.Zhang, G., Ge, Y., Pan, X., Afsharzadeh, M.S., Ghalandari, M., 2022. Optimization of energy consumption of a green building using PSO-SVM algorithm. Sustain Energy Technol Assess 53, 102667.Zhou, G., Zhou, Y., Huang, H., Tang, Z., 2019. Functional networks and applications: A survey. Neurocomputing 335, 384–399.478847819Machine learningOptimizationNano insulationGreen house gasesEnergy savingPublicationORIGINALThermal conductivity improvement in a green building with Nano insulations using machine learning methods.pdfThermal conductivity improvement in a green building with Nano insulations using machine learning methods.pdfArtículosapplication/pdf2166535https://repositorio.cuc.edu.co/bitstreams/b97f85d0-d7db-4053-af43-3731544b1447/download1272d4e32154d6216dbb759a27ff1f9dMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/7d2b8ac9-b7eb-4382-a8cf-9cfe68276db5/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTThermal conductivity improvement in a green building with Nano insulations using machine learning methods.pdf.txtThermal conductivity improvement in a green building with Nano insulations using machine learning methods.pdf.txtExtracted texttext/plain39990https://repositorio.cuc.edu.co/bitstreams/887ebef4-3c73-4ef8-a25e-d7f3e23a9854/downloadaf6435e74464516dc53210e17c17a0f9MD53THUMBNAILThermal conductivity improvement in a green building with Nano insulations using machine learning methods.pdf.jpgThermal conductivity improvement in a green building with Nano insulations using machine learning methods.pdf.jpgGenerated Thumbnailimage/jpeg16189https://repositorio.cuc.edu.co/bitstreams/108396ed-3e6f-48b2-9928-de6ce45bd229/download33334bad2b57cb27923f6cf7a6488596MD5411323/10496oai:repositorio.cuc.edu.co:11323/104962024-09-17 10:52:56.278https://creativecommons.org/licenses/by-nc-nd/4.0//© 2023 The Authors. Published by Elsevier Ltd.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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