Open Access

ARTICLE

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

Mohsen Sharifpur^1,2, Mohamed Salem³, Yonis M Buswig⁴, Habib Forootan Fard⁵, Jaroon Rungamornrat^6,*

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:

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

Received 25 July 2022; Accepted 13 September 2022; Issue published 28 December 2022

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.

---

Keywords

---