dc.creator | Ghalandari, Mohammad | |
dc.creator | Mukhtar, Azfarizal | |
dc.creator | Hizam Md Yasir, Ahmad Shah | |
dc.creator | Alkhabbaz, Ali | |
dc.creator | Alviz Meza, Anibal | |
dc.creator | Cardenas Escorcia, Yulineth | |
dc.creator | Binh, Le Nguyen | |
dc.date | 2023-09-18T16:18:16Z | |
dc.date | 2023-09-18T16:18:16Z | |
dc.date | 2023 | |
dc.date.accessioned | 2023-10-03T19:07:38Z | |
dc.date.available | 2023-10-03T19:07:38Z | |
dc.identifier | 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 | https://hdl.handle.net/11323/10496 | |
dc.identifier | 10.1016/j.egyr.2023.03.123 | |
dc.identifier | 2352-4847 | |
dc.identifier | Corporación Universidad de la Costa | |
dc.identifier | REDICUC - Repositorio CUC | |
dc.identifier | https://repositorio.cuc.edu.co/ | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9167987 | |
dc.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. | |
dc.format | 8 páginas | |
dc.format | application/pdf | |
dc.format | application/pdf | |
dc.language | eng | |
dc.publisher | Elsevier Ltd. | |
dc.publisher | United Kingdom | |
dc.relation | Energy Reports | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | Azmi, N.A., Arıcı, M., Baharun, A., 2021. A review on the factors influencing
energy efficiency of mosque buildings. J Clean Prod 292, 126010. | |
dc.relation | Bagheri-Esfeh, H., Dehghan, M.R., 2022. Multi-objective optimization of setpoint
temperature of thermostats in residential buildings. Energy Build 261,
111955. | |
dc.relation | 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. | |
dc.relation | Charbuty, B., Abdulazeez, A., 2021. Classification based on decision tree algorithm
for machine learning. J Appl Sci Technol Trends 2, 20–28. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | Felius, L.C., Dessen, F., Hrynyszyn, B.D., 2020. Retrofitting towards energyefficient homes in European cold climates: a review. Energy Effic 13,
101–125. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | Hasan, A., 1999. Optimizing insulation thickness for buildings using life cycle
cost. Appl. Energy 63, 115–124. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | Kharrufa, S.N., Noori, F., 2022. A review of thermal design for buildings in hot
climates. Pertanika J Sci Technol 30. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | Kunelbayev, M., Amirgaliyev, Y., Sundetov, T., 2022. Improving the efficiency
of environmental temperature control in homes and buildings. Energies 15,
8839. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | Mercan, H., Çelen, A., Taner, T., 2022. Thermophysical and rheological properties
of unitary and hybrid nanofluids. Adv. Nanofluid Heat Transf. Elsevier
95–129. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | Ozel, M., 2011. Thermal performance and optimum insulation thickness of
building walls with different structure materials. Appl Therm Eng 31,
3854–3863. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | Suerdem, K., Taner, T., Acikgoz, O., Dalkilic, A.S., Panchal, H., 2023. Performance
of refrigerants employed in rooftop air-conditioners. J Build Eng 106301. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | 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. | |
dc.relation | Zeng, A., Ho, H., Yu, Y., 2020. Prediction of building electricity usage using
Gaussian process regression. J Build Eng 28, 101054. | |
dc.relation | 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. | |
dc.relation | Zhou, G., Zhou, Y., Huang, H., Tang, Z., 2019. Functional networks and
applications: A survey. Neurocomputing 335, 384–399. | |
dc.relation | 4788 | |
dc.relation | 4781 | |
dc.relation | 9 | |
dc.rights | /© 2023 The Authors. Published by Elsevier Ltd. | |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) | |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | http://purl.org/coar/access_right/c_abf2 | |
dc.source | https://www.sciencedirect.com/science/article/pii/S2352484723003608 | |
dc.subject | Machine learning | |
dc.subject | Optimization | |
dc.subject | Nano insulation | |
dc.subject | Green house gases | |
dc.subject | Energy saving | |
dc.title | Thermal conductivity improvement in a green building with Nano insulations using machine learning methods | |
dc.type | Artículo de revista | |
dc.type | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.type | Text | |
dc.type | info:eu-repo/semantics/article | |
dc.type | http://purl.org/redcol/resource_type/ART | |
dc.type | info:eu-repo/semantics/publishedVersion | |
dc.type | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |