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

Zainab Hamed Alsidairi

doi:10.14419/ijet.v7i4.35.22336

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.

Highlights

The transport sector is growing rapidly in the Middle East
This part of research focuses on the artificial neural network (ANN) and multiple liner regression models to forecast the energy consumption within the transportation sector in GCC
With comparison being made to the set international air quality limits, we can deduce that the atmospheric condition in the town of Mansoria ignored the limits with only Sulphur Oxide being the lowest

Summary

Introduction

The transport sector is growing rapidly in the Middle East. According to numerous reports, the fuel efficiency of public and private transport is extremely low. As to support energy consumption related policies and research, energy demand models are needed on multiple liner regression and ANN methods These models help in getting accurate results of the amount of energy consumed in the transportation sector. Activities that are human-related usually the emission of primary pollutants like airborne chemicals that include non-methane hydrocarbons (THC) and methane (CH4), heavy metals, carbon monoxide, carbon dioxide, hydrogen sulfide, nitrogen oxide (NO) and Sulphur. Their subsequent interaction with the environment and the reactions of photochemical in the atmosphere causes secondary pollutants which include the Ozone (O3). The population has risen to over 3 million and the fleet of vehicles is at about 1 million with an expectation of the number increasing

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Results

Discussion

Conclusion