Publication:
The prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCC

dc.contributor.authorALSidairi Z.H.en_US
dc.contributor.authorid57205234565en_US
dc.date.accessioned2023-05-29T06:55:06Z
dc.date.available2023-05-29T06:55:06Z
dc.date.issued2018
dc.description.abstractKnowing how energy consumption correlates with transport sector in GCC can offer crucial strategies for planning and implementing policies in this sector. Therefore, an accurate prediction of energy consumption in transport and precise planning in energy consumption so as to effectively control the energy demand in the transport sector is crucial. Air pollution and public health are two of the most vital environmental issues. Urbanization, economic development, the growth of population, transportation, and energy consumption are viewed as the common factors that cause air pollution in towns and cities. The goal of this study is to use multiple liner regression (MLS) and artificial neural network (ANN) models for the prediction of energy consumption for the transport sector in GCC. Data on how energy is used in the transportation sector was incorporated as the output variable of predictive models. Moreover, this paper will discuss how advanced technology can come in to solve problems related to transport in the GCC. � 2018 Authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14419/ijet.v7i4.35.22336
dc.identifier.epage106
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85059230397
dc.identifier.spage98
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85059230397&doi=10.14419%2fijet.v7i4.35.22336&partnerID=40&md5=90668093ca8da61bee835c53ec3969ed
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24075
dc.identifier.volume7
dc.publisherScience Publishing Corporation Incen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Engineering and Technology(UAE)
dc.titleThe prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCCen_US
dc.typeArticleen_US
dspace.entity.typePublication
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