Modeling CO_(2)Emission in Residential Sector of Three Countries in Southeast of Asia by Applying Intelligent Techniques  

作　　者：Mohsen Sharifpur Mohamed Salem Yonis M Buswig Habib Forootan Fard Jaroon Rungamornrat 

机构地区：[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,China [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

出　　处：《Computers, Materials & Continua》2023年第3期5679-5690,共12页计算机、材料和连续体（英文）

基　　金：This work was funded by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme No.FRGS/1/2020/TK0/UNIMAS/03/4.

摘　　要：Residential sector is one of the energy-consuming districts of countries that causes CO_(2)emission in large extent.In this regard,this sector must be considered in energy policy making related to the reduction of emission of CO_(2)and other greenhouse gases.In the present work,CO_(2)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 CO_(2)emission is considered as the model’s output.The obtained R^(2) 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.

关 键 词：CO_(2)emission GMDH MLP intelligent techniques energy consumption 

分 类 号：X32[环境科学与工程—环境工程]