Forecasting the gasoline consumption in Iran’s transportation sector by ARIMA method

Document Type : Research Paper

Authors

1 Department of Energy Engineering and Physics, Energy System Engineering, Amirkabir University, Tehran, Iran

2 Department of Energy Engineering and Physics, Amirkabir University, Tehran, Iran

3 Department of Energy Engineering and Physics, Energy System Engineering, Amirkabir University, Tehran, Iran

4 Department of Energy Engineering and Physics, energy system Engineering, Amirkabir University, Tehran, Iran

Abstract

Transportation is one of the important bases of the national economy of any country. The development of the transportation sector has been accompanied by economic growth. In developing countries, the development of the transportation sector and the increasing number of vehicles increase energy consumption in this sector. Therefore, the management and energy supply of this sector are two of the main priorities of the governments in these countries. In this research, taking into account the data related to the gross domestic product, the number of gasoline cars produced, the number of passengers within and outside the province, and the price of gasoline, a regression equation was written using the least squares method to determine the effect of these components on consumption. Gasoline should be evaluated. Furthermore, with Iran's gasoline consumption data from 1962 to 2021, we have forecast the gasoline consumption between 2022 and 2031 with the ARIMA method. The research results show that between 2021 and 2022, Iran's gasoline consumption had a downward trend; its amount was -0.45%; and it had an upward trend from 2023 to 2031; it grew by 52.09% between these years.

Keywords


[1] Jia, S., et al., Review of Transportation and Energy Consumption Related Research. Journal of Transportation Systems Engineering and Information Technology, 2009. 9(3): p. 6-16.
[2] Mačiulis, A., A.V. Vasiliauskas, and G. Jakubauskas, The impact of transport on the competitiveness of national economy. Transport, 2009. 24(2): p. 93-99.
[3] Zarifi, F., et al., Current and future energy and exergy efficiencies in the Iran’s transportation sector. Energy Conversion and Management, 2013. 74: p. 24-34.
[4] Sandoval-García, E., Y. Matsumoto, and D. Sánchez-Partida, Data and energy efficiency indicators of freight transport sector in Mexico. Case Studies on Transport Policy, 2021. 9(3): p. 1336-1343.
[5] Mohsin, M., et al., Integrated effect of energy consumption, economic development, and population growth on CO2 based environmental degradation: a case of transport sector. Environmental Science and Pollution Research, 2019. 26(32): p. 32824-32835.
[6] Samaras, Z. and I. Vouitsis, Energy Consumption of Transport Modes, in International Encyclopedia of Transportation, R. Vickerman, Editor. 2021, Elsevier: Oxford. p. 71-84.
[7] www.iea.org.
[8] Moshiri, S., Consumer responses to gasoline price and non-price policies. Energy Policy, 2020. 137: p. 111078.
[9] Sadeghi, M. and H. Mirshojaeian Hosseini, Integrated energy planning for transportation sector—A case study for Iran with techno-economic approach. Energy Policy, 2008. 36(2): p. 850-866.
[10] Ghorbani, N., A. Aghahosseini, and C. Breyer, Assessment of a cost-optimal power system fully based on renewable energy for Iran by 2050–Achieving zero greenhouse gas emissions and overcoming the water crisis. Renewable Energy, 2020. 146: p. 125-148.
[11] Moshiri, S. Energy price reform and energy efficiency in Iran. in IAEE Energy Forum. 2013. International Association for Energy Economics Cleveland, OH.
[12] Statistics of the consumption of energy-generating petroleum products in the year 2018(in persian). 2018: National iranian oil refining and distibution company (NIORDC).
[13] Omrani, H., K. Shafaat, and A. Alizadeh, Integrated data envelopment analysis and cooperative game for evaluating energy efficiency of transportation sector: a case of Iran. Annals of Operations Research, 2019. 274(1): p. 471-499.
[15] Hosseini Nasab, E., et al., An analysis of energy consumption in transportation and industrial sectors-a multiplicative LMDI approach with application to Iran. Iranian Economic Review, 2012. 16(32): p. 1-17.
[16] About energy subsidy in Iran, hidden subsidy and its considerations (in Persia). 2017: Islamic Parliament Research Center of Iran. p. 1-46.
[18] Cervero, R., Short-run forecasting of highway gasoline consumption in the United States. Transportation Research Part A: General, 1985. 19(4): p. 305-313.
[19] Ediger, V.Ş. and S. Akar, ARIMA forecasting of primary energy demand by fuel in Turkey. Energy policy, 2007. 35(3): p. 1701-1708.
[20] Li, Z., J.M. Rose, and D.A. Hensher, Forecasting automobile petrol demand in Australia: An evaluation of empirical models. Transportation Research Part A: Policy and Practice, 2010. 44(1): p. 16-38.
[21] Akpinar, M. and N. Yumusak. Forecasting household natural gas consumption with ARIMA model: A case study of removing cycle. in 2013 7th International Conference on Application of Information and Communication Technologies. 2013.
[22] Mahia, F., et al. Forecasting Electricity Consumption using ARIMA Model. in 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI). 2019.
[23] Güngör, B.O., H.M. Ertuğrul, and U. Soytaş, Impact of Covid-19 outbreak on Turkish gasoline consumption. Technological Forecasting and Social Change, 2021. 166: p. 120637.
[24] DASTJERDI, A.M. and B.N. Araghi, Fuel Consumption Management in the Transportation Sector in Iran. Dear Readers, Welcome to the 3 rd issue, volume 2, of our online-peer-reviewed International Journal of the Society of Transportation and Traffic Studies. Four issues of the journal are published This issue contains 4 technical papers: two on urban mobility issues, the first presents a study on how to maximize urban space and increase, 2005. 4223(3632): p. 56.
[25] Zhang, G.P., Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 2003. 50: p. 159-175.
[26] Brockwell, P.J. and R.A. Davis, Introduction to time series and forecasting. 2002: Springer.
[27] Burton, A.L., OLS (Linear) regression. The Encyclopedia of Research Methods in Criminology and Criminal Justice, 2021. 2: p. 509-514.
[28] Statistical yearbook of Iran. 2020: Statistical Center of Iran.
[29] Fattah, J., et al., Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 2018. 10: p. 1847979018808673.
[30] Oğuz, M.E., Forecasting Turkey's sectoral energy demand. 2013, Middle East Technical University.
[31] https://www.tgju.org/archive/price_dollar_rl. [cited 2022 November,14].