Forecasting Gasoline Consumption in Iran using Deep Learning Approaches

Document Type : Research Paper

Authors

1 Department of Economics, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Department of Computer and Information Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran

3 Faculty Statistics, Mathematics and Computer, Allameh Tabataba'i University, Tehran, Iran.

Abstract

Gasoline consumption is one of the challenging issues of energy management in Iran. The deficit of domestic production and the need for imports on one hand, and the impact of its consumption on macro-and micro-economic variables, on the other hand, cause gasoline consumption management has become more important. The more accurate, prediction of the trend of gasoline consumption is the more successful consumption management will be. Since gasoline consumption is affected by several parameters and factors, so, forecasting its consumption with high accuracy is difficult. In this paper, one recursive competitive learning method and two deep learning methods are utilized to provide more accurate forecasting of gasoline consumption. Due to the impact of gasoline consumption patterns on seasonal changes, climate, and holidays, different periods are used for training the learning of these approaches, and their efficiency is compared in terms of the standard error metrics. The comparison results show the deep learning approaches and the training patterns with 12 months result in more accurate predictions. Finally, using the best approach and obtained setting, the gasoline consumption in Iran is predicted for the next years, which shows that gasoline consumption will grow 22 percent by 2027.

Keywords

Main Subjects

References

Abrishami, H., Mehrara, M., Ahrari, M., & Varahrami, V. (2010). A Hybrid Intelligent System for Forecasting Gasoline Price. Iranian Economic Review, 15(27), 13-31.
Asgharizadeh, E., & Taghizadeh, M. R. (2012). A Hierarchical Artificial Neural Network For Gasoline Demand Forecast of Iran. International Journal of Humanities, 19(1), 1-13.
Assari, M. R., Ghanbarzadeh, A., Assareh, E., & Behrang, M. A. (2009, June). Coal Demand Estimating In Iran Based on Socio-Economic Indicators Using Particle Swarm Optimisation and Genetic Algorithm. 2009 7th IEEE International Conference on Industrial Informatics. Cardiff: IEEE.
Azadeh, A., Arab, R., & Behfard, S. (2010). An Adaptive Intelligent Algorithm for Forecasting Long Term Gasoline Demand Estimation: The Cases of USA, Canada, Japan, Kuwait and Iran. Expert Systems with Applications, 37(12), 7427-7437.
Azadeh, A., Arab, R., & Behfard, S. (2010). An Adaptive Intelligent Algorithm for Forecasting Long Term Gasoline Demand Estimation: The Cases of USA, Canada, Japan, Kuwait and Iran. Expert Systems with Applications, 37(12), 7427-7437.
Azadeh, A., Boskabadi, A., & Pashapour, S. (2015). A Unique Support Vector Regression for Improved Modelling and Forecasting of Short-Term Gasoline Consumption in Railway Systems. International Journal of Services and Operations Management, 21(2), 217-237.
Azadeh, A., Boskabadi, A., & Pashapour, S. (2015). A Unique Support Vector Regression for Improved Modelling and Forecasting of Short-Term Gasoline Consumption in Railway Systems. International Journal of Services and Operations Management, 21(2), 217-237.
Azadeh, A., Mirjalili, M., Sheikhalishahi, M., & Nassiri, S. (2012). An Integrated Genetic Algorithm-Conventional Regression-Analysis Of Variance For Improvement Of Gasoline Demand Estimation. International Journal of Industrial and Systems Engineering, 11(3), 205-224.
Broadhead, J., & Killmann, W. (2008). Forests and Energy: Key Issues (154). Rome: Food & Agriculture Org.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation. arXiv preprint arXiv, Retrieved from           https://arxiv.org/pdf/1406.1078.pdf?source=post_page
Diebold, F. X., & Mariano, R. S. (1995). Comparing Predictive Accuracy. Journal of Business and Economic Statistics, 13, 253-63.
Fu, R., Zhang, Z., & Li, L. (2016). Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction. 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). Wuhan: IEEE.
Graves, A. (2013). Generating Sequences with Recurrent Neural Networks. arXiv preprint arXiv, Retrieved from https://arxiv.org/pdf/1308.0850
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
Hrasko, R., Pacheco, A. G., & Krohling, R. A. (2015). Time Series Prediction Using Restricted Boltzmann Machines and Backpropagation. Procedia Computer Science, 55, 990-999.
Hussain, A. J., Knowles, A., Lisboa, P. J., & El-Deredy, W. (2008). Financial Time Series Prediction Using Polynomial Pipelined Neural Networks. Expert Systems with Applications, 35(3), 1186-1199.
Kazemi, A., Ganjavi, H. S., Menhaj, M., Mehregan, M., Taghizadeh, M., & Asl, A. F. (2009). A Multi-Level Artificial Neural Network for Gasoline Demand Forecasting of Iran. 2009 Second International Conference on Computer and Electrical Engineering. Dubai: IEEE.
Kazemi, A., Shakouri, H. G., Menhaj, M. B., Mehregan, M. R., & Neshat, N. (2010). A Hierarchical Artificial Neural Network for Transport Energy Demand Forecast: Iran Case Study. Neural Network World, 20(6), 761-772.
Kim, T. Y., & Cho, S. B. (2019). Predicting Residential Energy Consumption Using CNN-LSTM Neural Networks. Energy, 182, 72-81.
Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2017). Short-term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid, 10(1), 841-851.
Lee, C. M., & Ko, C. N. (2009). Time Series Prediction Using RBF Neural Networks with A Nonlinear Time-Varying Evolution PSO Algorithm. Neurocomputing, 73(1-3), 449-460.
Mustaffa, Z., & Yusof, Y. (2012). Lévy Mutation in Artificial Bee Colony Algorithm for Gasoline Price Prediction. Knowledge Management International Conference (KMICe) 2012, Retrieved from https://repo.uum.edu.my/id/eprint/10963/1/CR180.pdf
Nasr, G. E., Badr, E. A., & Joun, C. (2002, May). Cross Entropy Error Function in Neural Networks: Forecasting Gasoline Demand. FLAIRS Conference, Retrieved from       https://laur.lau.edu.lb:8443/xmlui/bitstream/handle/10725/6723/Cross.pdf?sequence=1
Pelayo, D. R., Pacheco, J. A. C., & Miralles-Pechuán, L. (2016). Emotion Analysis in Gasoline Consumption in Mexico using Machine Learning. Advances in Computational Linguistics, 8, 1-20.
Predić, B., Madić, M., Roganović, M., Kovačević, M., & Stojanović, D. (2016). Prediction of Passenger Car Fuel Consumption Using Artificial Neural Network: A Case Study in The City of Niš. Facta Universitatis, Series: Automatic Control and Robotics, 1(2), 105-116.
Rahimi-Ajdadi, F., & Abbaspour-Gilandeh, Y. (2011). Artificial Neural Network and Stepwise Multiple Range Regression Methods for Prediction of Tractor Fuel Consumption. Measurement, 44(10), 2104-2111.
Report of Iran‘s Energy Balance Sheet. (2002-2018). Retrieved from https://www.iea.org/data
Sapankevych, N. I., & Sankar, R. (2009). Time Series Prediction Using Support Vector Machines: A Survey. IEEE Computational Intelligence Magazine, 4(2), 24-38.
Suganthi, L., & Samuel, A. A. (2012). Energy Models for Demand Forecasting-A Review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240.
Varsta, M., Heikkonen, J., Lampinen, J., & Millán, J. D. R. (2001). Temporal Kohonen Map and The Recurrent Self-Organizing Map: Analytical and Experimental Comparison. Neural Processing Letters, 13(3), 237-251.
Wang, X., Yang, K., & Kalivas, J. H. (2020). Comparison of Extreme Learning Machine Models for Gasoline Octane Number Forecasting by Near-Infrared Spectra Analysis. Optik, 200, 163325.
Wu, L. X., & Lee, S. J. (2020). A Deep Learning-Based Strategy to the Energy Management-Advice for Time-of-Use Rate of Household Electricity Consumption. Journal of Internet Technology, 21(1), 305-311.
Iranian Economic Review
Volume 27, Issue 2
July 2023
Pages 347-376

Files

Share

How to cite

Statistics