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

  • 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.

  • References

    1. [1] Alshamsi, A., & Diabat, A. A Genetic Algorithm for Reverse Logistics network design: A case study from the GCC. Journal of Cleaner Production, 151, 652-669. (2017).

      [2] Bätzner, A. N., & Stephenson, M. L. 4 Towards an integrated transport network in the GCC region. International Tourism Development and the Gulf Cooperation Council States: Challenges and Opportunities, 76. (2017).

      [3] Deakin, E. Sustainable Development and Sustainable Transportation: Strategies for Economic Prosperity, Environmental Quality, and Equity. Berkeley: University of California at Berkeley Institute of Urban and Regional Development,(2001).

      [4] Haldenbieln, S. ‘Fuel price determination in transportation sector using predicted energy and transport demand’, Energy Policy, Vol. 34, pp.3078–3086, (2006)

      [5] Himanshu, A.A. and Lester, C.H. ‘Electricity demand for Sri Lanka: a time series analysis’, Energy, Vol. 33, pp.724–739, (2008).

      [6] Jacobson, S., & King, D, Fuel saving and ridesharing in the US: Motivations, limitations, and opportunities. Transportation Research Part D: Transport and Environment, 14-21, (2009).

      [7] Stephenson, M. L., & Bätzner, A. N. , Towards an integrated transport network in the GCC region: Fostering tourism and regional cooperation. In International Tourism Development and the Gulf Cooperation Council States (pp. 76-91). Routledge, (2017).

      [8] T. Limanond, S. Jomnonkwao and A. Srikaew. ,"Projection of future transport energy demand of Thailand," Energy Policy, vol. 39, pp. 2754-2763, (2011).

      [9] W. Jin-ming and L. Xin-heng, "The forecast of energy demand on artificial neural network," in International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China, (2009).

      [10] Y. S. Murat and H. Cylan, "Use of artificial neural network for transport energy demand modeling," Energy Policy, vol. 34, pp. 3165-3172, (2006).

      [11] Z. W. Geem.,"Transport energy demand modeling of South Korea using artificial neural networks," Energy Policy, vol. 39, pp. 4644-4650, (2011).

      [12] Zhang, M., Mu, H., Li, G. and Ning, Y, ‘Forecasting the transport energy demand based on PLSR method in China’, Energy, Vol. 34, pp.1396–1400, (2009).

      [13] Ahmed Al Shamsi, and Diabat, A. (2017). Genetic algorithm for the design of reverse logistics networks: case study of the GCC Kuwait. Journal of Cleaner Production,

      [14] Abdel Wahab, S. B. (2014). Forecasting Ozone Levels: A Statistical Model of Ozone Levels: A Statistical Model of Environmental Sciences and Pollution.

      [15] Amir Alhhaddad, E. (2013). Analysis of emission patterns for air pollution near Kuwait Refinery. Search the British Journal.

      [16] Arslan, P and Saral, S.D. Evaluation of VOC release odor from the landfill at the Turkish Center in Istanbul using modeling methods. Journal of Hazardous Materials (2009).

      [17] B.R. Jurgar, T. B. Assessment of emissions and air quality in major cities. Air Environment. Vol. 7th Ed. 3rd McGrew UK(2008).

      [18] Berghlia. B. M. Environmental Efficiency Analysis of ENI Refinery. Journal of Clean Production,(2002)..

      [19] El-Fadel, A. M. Flue Emissions from Desalination Plant: Sensitivity analysis of parameters for exposure assessment. Seawater desalination, (2005).

      [20] Grivas, A. Synthetic neural network model to predict a 10-hour concentration in Athens, Greece. Air Environment, (2016).

      [21] Hnovová, J.Š. and Miller, P Ambient air trends and sediment trends in rural stations in the Czech Republic during the period 2001-2004. Air Environment, (2004).

      [22] K. Simkhada, K. M.Evaluate the ambient air quality of the Bishnumati Pass in Kathmandu. International Journal of Environmental Sciences and Technology, (2015).

      [23] K.Zhou, Y.Y.Guangzhou Environmental Quality Environmental Assessment, Environmental Science, (2007).

      [24] Khan, S. M.-S. Monitoring and simulation of trends in primary and secondary air pollution precursors: State of Kuwait. International Journal of Chemical Engineering, (2010).

      [25] L. Zhen, F. L. Families are willing to reduce the risk of pollution in the Poyang Lake area in southern China. Geochemical exploration, (2011).

      [26] P. Kassomenos, A.K.Assessment of air quality in the urban Mediterranean environment severely polluted according to the air quality index. Environmental indicators, (2012).

      [27] Robin, S and Taylor, P Simulation of the neural network for the spatial distribution of air pollutants. Air Environment, (2012).

      [28] R.S. Ettouney, J. Z.-R.Air pollution data were evaluated from two monitoring stations in Kuwait. Environmental chemistry, (2010).

      [29] Saleh Mohammed Alawi,Alongside the decline of the main component and artificial neural networks, ozone can be predicted at the ground level more accurately. Environmental modeling and software, (2008).

      [30] SivacoumaRichard, A. B.Air Pollution Model and Performance Assessment of the Industrial Complex Model. Direct science, (2001).

      [31] Stedman, J.Compare the forecasts published by the UK air quality strategy in 2000 with recent standards and model assessments. Air Environment. (2006).

      [32] Stephenson, M. L. 4 towards an integrated transport network in the GCC region. International Development of Tourism and the GCC Countries: Challenges and Opportunities, 76, (2017).

      [33] Miller, P and Mohsenian H. A general description of the regional smart grid of the GCC countries. 360 degrees in Smart City (pages 301-313). Springer., (2016).

      [34] Nelly, R. K. A comparative assessment of the ambient air quality of two typical coastal cities in the Mediterranean Sea in Greece. Optical, (2005).

      [35] Haldenbieln, S. "Use energy and transport means needs to fuel prices in the transport sector", Energy Policy, vol. 34, p. 3078-3086.and the Countries of the Gulf Cooperation Council (pp. 76-91). Routledge(2006).

      [36] Taylor, L and A. Srikaew. "Future of demand energy demand in transport in Thailand", Energy Policy, vol. 39, p. 2754-2763, (2011).

  • Downloads

  • 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