Optimum estimation and forecasting of gasoline consumption in Iran's national oil refining and distribution company

https://doi.org/10.35912/ijfam.v6i4.2492
Published: Apr 9, 2025

Abstract:

Purpose: This paper presents an accurate estimation and forecasting of gasoline consumption. This is vital for the policy and decision-making process in the energy sector.

Method: A hybrid data-driven model based on Artificial Neural Network (ANN) and an autoregressive integrated moving average (ARIMA) approach was developed for optimum estimation and forecasting of gasoline consumption. The proposed hybrid ARIMA-ANN approach considers six lagged variables and one forecasted value provided by the ARIMA process. The ANN trains and tests data with a multi-layer perceptron (MLP) approach, which has the lowest Mean Absolute Percentage Error (MAPE). To show the applicability and superiority of the proposed hybrid approach, daily available data were collected for 7 years (2015–2021) in Iran.

Results: The acquired results show a high accuracy of about 94.27% using the proposed hybrid ARIMA-ANN method. The results of the proposed model are compared with respect to the regression models and the ARIMA process.

Conclusions: Analyzing consumption patterns can provide insights into consumer behavior, enabling NIORDC to tailor its services and marketing strategies more effectively.

Limitations: Eliminating subsidies from gasoline prices has led to the appearance of noisy data in gasoline consumption in Iran's National Oil Refining and Distribution Company.

Contribution: The outcome of this paper justifies the capability of the proposed hybrid ARIMA-ANN approach in accurate forecasting of gasoline consumption.

Keywords:
1. Gasoline consumption
2. Artificial Neural Network
3. ARIMA
4. Forecasting
5. Multi Layer Perceptron
Authors:
1 . Afshar Bazyar
Malek Ashtar University of Technology, Iran
2 . Morteza Abbasi
Malek Ashtar University of Technology, Iran
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How to Cite
Bazyar, A., & Abbasi, M. (2025). Optimum estimation and forecasting of gasoline consumption in Iran’s national oil refining and distribution company. International Journal of Financial, Accounting, and Management, 6(4), 509–524. https://doi.org/10.35912/ijfam.v6i4.2492
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Issue & Section
Vol. 6 No. 4 (2025): March
Articles
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  2. Austina, N., Senthilkumar, P., & Kanthavelkumaran, N. (2013). Artificial neural network involved in the action of optimum mixed refrigerant (domestic refrigerator).
  3. Azadeh, A., Asadzadeh, S., & Ghanbari, A. (2010). An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments. Energy Policy, 38(3), 1529-1536. https://doi.org/10.1016/j.enpol.2009.11.036.
  4. Azadeh, A., Babazadeh, R., & Asadzadeh, S. (2013). Optimum estimation and forecasting of renewable energy consumption by artificial neural networks. Renewable and Sustainable Energy Reviews, 27, 605-612. https://doi.org/10.1016/j.rser.2013.07.007.
  5. Díaz-Robles, L. A., Ortega, J. C., Fu, J. S., Reed, G. D., Chow, J. C., Watson, J. G., & Moncada-Herrera, J. A. (2008). A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmospheric Environment, 42(35), 8331-8340. https://doi.org/10.1016/j.atmosenv.2008.07.020.
  6. Fan, D., Sun, H., Yao, J., Zhang, K., Yan, X., & Sun, Z. (2021). Well production forecasting based on ARIMA-LSTM model considering manual operations. Energy, 220, 119708. https://doi.org/10.1016/j.energy.2020.119708.
  7. Fathi, S., Eftekhari Yazdi, M., & Adamian, A. (2020). Estimation of contact heat transfer between curvilinear contacts using inverse method and group method of data handling (GMDH)-type neural networks. Heat and Mass Transfer, 56, 1961-1970. https://doi.org/10.1007/s00231-020-02832-x.
  8. Forozandeh, M. (2021). The effect of supply chain management challenges on research and development projects using Fuzzy DEMATEL and TOPSIS approach. Annals of Management and Organization Research, 2(3), 175-190. https://doi.org/10.35912/amor.v2i3.801.
  9. Ghahnavieh, A. E. (2019). Time series forecasting of styrene price using a hybrid ARIMA and neural network model. Independent Journal of Management & Production, 10(3), 915-933. https://doi.org/10.14807/ijmp.v10i3.877.
  10. Ho, L. V., Nguyen, D. H., Mousavi, M., De Roeck, G., Bui-Tien, T., Gandomi, A. H., & Wahab, M. A. (2021). A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks. Computers & Structures, 252, 106568. https://doi.org/10.1016/j.compstruc.2021.106568.
  11. Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied soft computing, 11(2), 2664-2675. https://doi.org/10.1016/j.asoc.2010.10.015.
  12. Mbamalu, E. I., Chike, N. K., Oguanobi, C. A., & Egbunike, C. F. (2023). Sustainable supply chain management and organisational performance: Perception of academics and practitioners. Annals of Management and Organization Research, 5(1), 13-30. https://doi.org/10.35912/amor.v5i1.1758.
  13. Melina, M., Sukono, S., Napitupulu, H., Mohamed, N., Chrisnanto, Y. H., Hadiana, A. I., . . . Nabilla, U. (2024). COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 18(4), 2563-2576. https://doi.org/10.30598/barekengvol18iss4pp2563-2576.
  14. Ong, C.-S., Huang, J.-J., & Tzeng, G.-H. (2005). Model identification of ARIMA family using genetic algorithms. Applied Mathematics and Computation, 164(3), 885-912. https://doi.org/10.1016/j.amc.2004.06.044.
  15. Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing, volume 1: Explorations in the microstructure of cognition: Foundations: The MIT press.
  16. Tarafdar, H. M., & Ghadimi, N. (2014). Radial basis neural network based islanding detection in distributed generation.
  17. Viana, C., Oliveira, S., & Rocha, J. (2024). Introductory Chapter: Time Series Analysis. Time Series Analysis: Recent Advances, New Perspectives and Applications, 3-13.
  18. Wang, L., Zou, H., Su, J., Li, L., & Chaudhry, S. (2013). An ARIMA?ANN hybrid model for time series forecasting. Systems Research and Behavioral Science, 30(3), 244-259. https://doi.org/10.1002/sres.2179.
  19. Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences. PhD thesis, Committee on Applied Mathematics, Harvard University, Cambridge, MA.
  20. Zahedi, M., Abbasi, M., & Khanachah, S. N. (2020). Providing a lean and agile supply chain model in project-based organizations. Annals of Management and Organization Research, 1(3), 213-233. https://doi.org/10.35912/amor.v1i3.440.