The prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCC
Knowing 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...
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my.uniten.dspace-240752023-05-29T14:55:07Z The prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCC ALSidairi Z.H. 57205234565 Knowing 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. Final 2023-05-29T06:55:06Z 2023-05-29T06:55:06Z 2018 Article 10.14419/ijet.v7i4.35.22336 2-s2.0-85059230397 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059230397&doi=10.14419%2fijet.v7i4.35.22336&partnerID=40&md5=90668093ca8da61bee835c53ec3969ed https://irepository.uniten.edu.my/handle/123456789/24075 7 4 98 106 Science Publishing Corporation Inc Scopus |
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Knowing 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. |
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57205234565 ALSidairi Z.H. |
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ALSidairi Z.H. The prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCC |
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ALSidairi Z.H. |
title |
The prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCC |
title_short |
The prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCC |
title_full |
The prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCC |
title_fullStr |
The prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCC |
title_full_unstemmed |
The prediction of energy consumption using multivariate regression and artificial neural network models: Transport in the GCC |
title_sort |
prediction of energy consumption using multivariate regression and artificial neural network models: transport in the gcc |
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Science Publishing Corporation Inc |
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2023 |
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