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

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Computers, materials & Continua 2023年第3期74卷 5679-5690页

作者： mohsen Sharifpur mohamed Salem yonis m buswig Habib Forootan Fard Jaroon Rungamornrat Clean Energy Research Group Department of Mechanical and Aeronautical EngineeringEngineering IIIUniversity of PretoriaPretoriaSouth Africa Department of medical Research China Medical University HospitalChina Medical UniversityTaichungTaiwanChina School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong Tebal14300PenangMalaysia Institute of Sustainable and Renewable Energy ISuRE Faculty of EngineeringUniversityMalaysia Sarawak Department of Renewable Energies Faculty of New Sciences and TechnologiesUniversity of TehranTehranIran Center of Excellence in Applied mechanics and Structures Department of Civil EngineeringFaculty of EngineeringChulalongkorn UniversityBangkok10330Thailand

Residential sector is one of the energy-consuming districts of countries that causes CO_(2)emission in large *** this regard,this sector must be considered in energy policy making related to the reduction of emission of CO_(2)and other greenhouse *** 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 *** to modeling,data related to the energy consumption of these countries are represented,discussed,and ***,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 *** obtained R^(2) values for the generated models based on mLP and GmDH are 0.9987 and 0.9985,***,values of the Average Absolute Relative Deviation(AARD)of the regressions using the mentioned techniques are around 4.56%and 5.53%,*** 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

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