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Modeling CO2 Emission in Residential Sector of Three Countries in Southeast of Asia by Applying Intelligent Techniques

Mohsen Sharifpur1,2, Mohamed Salem3, Yonis M Buswig4, Habib Forootan Fard5, Jaroon Rungamornrat6,*

1 Clean Energy Research Group, Department of Mechanical and Aeronautical Engineering, Engineering III, University of Pretoria, Pretoria, South Africa
2 Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
3 School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal, 14300, Penang, Malaysia
4 Institute of Sustainable and Renewable Energy ISuRE, Faculty of Engineering, University, Malaysia Sarawak
5 Department of Renewable Energies, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
6 Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand

* Corresponding Author: Jaroon Rungamornrat. Email: email

Computers, Materials & Continua 2023, 74(3), 5679-5690. https://doi.org/10.32604/cmc.2023.034726

Abstract

Residential sector is one of the energy-consuming districts of countries that causes CO2 emission in large extent. In this regard, this sector must be considered in energy policy making related to the reduction of emission of CO2 and other greenhouse gases. In the present work, CO2 emission related to the residential sector of three countries, including Indonesia, Thailand, and Vietnam in Southeast Asia, are discussed and modeled by employing Group Method of Data Handling (GMDH) and Multilayer Perceptron (MLP) neural networks as powerful intelligent methods. Prior to modeling, data related to the energy consumption of these countries are represented, discussed, and analyzed. Subsequently, to propose a model, electricity, natural gas, coal, and oil products consumptions are applied as inputs, and CO2 emission is considered as the model’s output. The obtained R2 values for the generated models based on MLP and GMDH are 0.9987 and 0.9985, respectively. Furthermore, values of the Average Absolute Relative Deviation (AARD) of the regressions using the mentioned techniques are around 4.56% and 5.53%, respectively. These values reveal significant exactness of the models proposed in this article; however, making use of MLP with the optimal architecture would lead to higher accuracy.

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APA Style
Sharifpur, M., Salem, M., Buswig, Y.M., Fard, H.F., Rungamornrat, J. (2023). Modeling co2 emission in residential sector of three countries in southeast of asia by applying intelligent techniques. Computers, Materials & Continua, 74(3), 5679-5690. https://doi.org/10.32604/cmc.2023.034726
Vancouver Style
Sharifpur M, Salem M, Buswig YM, Fard HF, Rungamornrat J. Modeling co2 emission in residential sector of three countries in southeast of asia by applying intelligent techniques. Comput Mater Contin. 2023;74(3):5679-5690 https://doi.org/10.32604/cmc.2023.034726
IEEE Style
M. Sharifpur, M. Salem, Y.M. Buswig, H.F. Fard, and J. Rungamornrat "Modeling CO2 Emission in Residential Sector of Three Countries in Southeast of Asia by Applying Intelligent Techniques," Comput. Mater. Contin., vol. 74, no. 3, pp. 5679-5690. 2023. https://doi.org/10.32604/cmc.2023.034726



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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