The Prediction of Energy Consumption Using Multivariate Regression and Artificial Neural Network Models: Transport in the GCC

  • Authors

    • Zainab Hamed ALSidairi
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.35.22336
  • Artificial Neural Network Models, Energy Consumption, multiple liner regression, Multivariate Regression Models

  • Abstract

    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.

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  • How to Cite

    ALSidairi, Z. H. (2018). The Prediction of Energy Consumption Using Multivariate Regression and Artificial Neural Network Models: Transport in the GCC. International Journal of Engineering & Technology, 7(4.35), 98-106. https://doi.org/10.14419/ijet.v7i4.35.22336

    Received date: 2018-11-29

    Accepted date: 2018-11-29

    Published date: 2018-11-30